Navigation using points on splines

ABSTRACT

A system for navigating a host vehicle includes at least one electronic horizon processor to access a map representative of at least a road segment on which the host vehicle travels or is expected to travel, wherein the map includes one or more splines representative of road features associated with the road segment, localize the host vehicle relative to a drivable path for the host vehicle represented among the one or more splines, determine a set of points associated with the one or more splines based on the localization of the host vehicle relative to the drivable path for the host vehicle, and generate a navigation information packet including information associated with the one or more splines and the determined set of points relative to the one or more splines.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 63/001,733, filed on Mar. 30, 2020; and U.S. ProvisionalApplication No. 63/152,925, filed on Feb. 24, 2021. The foregoingapplications are incorporated herein by reference in their entireties.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera) and may also use informationobtained from other sources (e.g., from a GPS device, a speed sensor, anaccelerometer, a suspension sensor, etc.). At the same time, in order tonavigate to a destination, an autonomous vehicle may also need toidentify its location within a particular roadway (e.g., a specific lanewithin a multi-lane road), navigate alongside other vehicles, avoidobstacles and pedestrians, observe traffic signals and signs, and travelfrom one road to another road at appropriate intersections orinterchanges. Harnessing and interpreting vast volumes of informationcollected by an autonomous vehicle as the vehicle travels to itsdestination poses a multitude of design challenges. The sheer quantityof data (e.g., captured image data, map data, GPS data, sensor data,etc.) that an autonomous vehicle may need to analyze, access, and/orstore poses challenges that can in fact limit or even adversely affectautonomous navigation. Furthermore, if an autonomous vehicle relies ontraditional mapping technology to navigate, the sheer volume of dataneeded to store and update the map poses daunting challenges.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for vehicle navigation.

In an embodiment, a system may include at least one electronic horizonprocessor, which may include circuitry and a memory. The memory mayinclude instructions that when executed by the circuitry may cause theat least one electronic horizon processor to access a map representativeof at least a road segment on which the host vehicle travels or isexpected to travel, and receive an output provided by at least onevehicle sensor. The at least one vehicle sensor may include one or morecameras configured to capture images of an environment of the hostvehicle. The received output may include at least one image captured bythe one or more cameras. The instructions may also cause the at leastone electronic horizon processor to localize the host vehicle relativeto the map based on analysis of the at least one image captured by theone or more cameras. The instructions may further cause the at least oneelectronic horizon processor to determine an electronic horizon for thehost vehicle based on the localization of the host vehicle relative tothe map. The instructions may also cause the at least one electronichorizon processor to generate a navigation information packet includinginformation associated with the determined electronic horizon. Thenavigation information packet may include a header portion and avariable-sized payload portion. The header portion may specify whatinformation is included in the variable-sized payload portion. Theinstructions may further cause the at least one electronic horizonprocessor to output the generated navigation information packet to oneor more navigation system processors configured to cause the hostvehicle to execute at least one navigational maneuver based on theinformation included in the navigation information packet.

In an embodiment, a non-transitory computer readable medium may containinstructions that when executed by at least one electronic horizonprocessor, cause the at least one electronic horizon processor toperform operations including accessing a map representative of at leasta road segment on which a host vehicle travels or is expected to travel.The operations may also include receiving an output provided by at leastone vehicle sensor. The at least one vehicle sensor may include one ormore cameras configured to capture images of an environment of the hostvehicle. The received output may include at least one image captured bythe one or more cameras. The operations may further include localizingthe host vehicle relative to the map based on analysis of the at leastone image captured by the one or more cameras. The operations may alsoinclude determining an electronic horizon for the host vehicle based onthe localization of the host vehicle relative to the map. The operationsmay further include generating a navigation information packet includinginformation associated with the determined electronic horizon. Thenavigation information packet may include a header portion and avariable-sized payload portion. The header portion may specify whatinformation is included in the variable-sized payload portion. Theoperations may also include outputting the generated navigationinformation packet to one or more navigation system processorsconfigured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet.

In an embodiment, a system may include at least one electronic horizonprocessor, which may include circuitry and a memory. The memory mayinclude instructions that when executed by the circuitry may cause theat least one electronic horizon processor to access a map representativeof a road on which the host vehicle travels or is expected to travel. Inthe map, the road may be represented as an internode road segmentbetween two mapped nodes, and in the map, the internode road segment maybe further divided into a plurality of internode road sub-segments. Theinstructions may also the at least one electronic horizon processor toreceive an output provided by at least one vehicle sensor. The at leastone vehicle sensor may include one or more cameras configured to captureimages of an environment of the host vehicle, and the received outputmay include at least one image captured by the one or more cameras. Theinstructions may also cause the at least one electronic horizonprocessor to localize the host vehicle relative to at least one mappedfeature based on analysis of the at least one image captured by the oneor more cameras. The instructions may further cause the at least oneelectronic horizon processor to determine an electronic horizon for thehost vehicle based on the localization of the host vehicle relative tothe at least one mapped feature. The instructions may also cause the atleast one electronic horizon processor to determine a set of internoderoad sub-segments that are included in the electronic horizon. Theinstructions may further cause the at least one electronic horizonprocessor to generate one or more navigation information packetsincluding information associated with the set of internode roadsub-segments included in the electronic horizon. The instructions mayalso cause the at least one electronic horizon processor to output thegenerated one or more navigation information packets to one or morenavigation system processors configured to cause the host vehicle toexecute at least one navigational maneuver based on the informationincluded in the navigation information packet.

In an embodiment, a non-transitory computer readable medium may containinstructions that when executed by at least one electronic horizonprocessor, cause the at least one electronic horizon processor toperform operations including accessing a map representative of a road onwhich a host vehicle travels or is expected to travel. In the map, theroad may be represented as an internode road segment between two mappednodes, and in the map, the internode road segment may be further dividedinto a plurality of internode road sub-segments. The operations may alsoinclude receiving an output provided by at least one vehicle sensor. Theat least one vehicle sensor may include one or more cameras configuredto capture images of an environment of the host vehicle, and thereceived output may include at least one image captured by the one ormore cameras. The operations may further include localizing the hostvehicle relative to at least one mapped feature based on analysis of theat least one image captured by the one or more cameras. The operationsmay also include determining an electronic horizon for the host vehiclebased on the localization of the host vehicle relative to the at leastone mapped feature. The operations may further include determining a setof internode road sub-segments that are included in the electronichorizon. The operations may also include generating one or morenavigation information packets including information associated with theset of internode road sub-segments included in the electronic horizon.The operations may further include outputting the generated one or morenavigation information packets to one or more navigation systemprocessors configured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet

In an embodiment, a system may include at least one electronic horizonprocessor, which may include circuitry and a memory. The memory mayinclude instructions that when executed by the circuitry may cause theat least one electronic horizon processor to access a map representativeof at least a road segment on which the host vehicle travels or isexpected to travel. Points in the map may be referenced relative to aninitial map origin. The instructions may further cause the at least oneelectronic horizon processor to receive an output provided by at leastone vehicle sensor. The at least one vehicle sensor may include one ormore cameras configured to capture images of an environment of the hostvehicle, and the received output may include at least one image capturedby the one or more cameras. The instructions may further cause the atleast one electronic horizon processor to localize the host vehiclerelative to the map based on analysis of the at least one image capturedby the one or more cameras. The instructions may also cause the at leastone electronic horizon processor to determine an electronic horizon forthe host vehicle based on the localization of the host vehicle relativeto the map. The instructions may further cause the at least oneelectronic horizon processor to generate a navigation information packetincluding information associated with mapped features included in thedetermined electronic horizon. The instructions may also cause the atleast one electronic horizon processor to output the generatednavigation information packet to one or more navigation systemprocessors configured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet. The instructions may further cause the atleast one electronic horizon processor to detect a map origin changeevent. The instructions may also cause the at least one electronichorizon processor to in response to a detected map origin change event,determine an updated map origin and send to the one or more navigationsystem processors a notification indicative of a change from the initialmap origin to the updated map origin.

In an embodiment, a non-transitory computer readable medium may containinstructions that when executed by at least one electronic horizonprocessor, cause the at least one electronic horizon processor toperform operations including accessing a map representative of at leasta road segment on which the host vehicle travels or is expected totravel. Points in the map may be referenced relative to an initial maporigin. The operations may also include receiving an output provided byat least one vehicle sensor. The at least one vehicle sensor may includeone or more cameras configured to capture images of an environment ofthe host vehicle, and the received output may include at least one imagecaptured by the one or more cameras. The operations may further includelocalizing the host vehicle relative to the map based on analysis of theat least one image captured by the one or more cameras. The operationsmay also include determining an electronic horizon for the host vehiclebased on the localization of the host vehicle relative to the map. Theoperations may further include generating a navigation informationpacket including information associated with mapped features included inthe determined electronic horizon. The operations may also includeoutputting the generated navigation information packet to one or morenavigation system processors configured to cause the host vehicle toexecute at least one navigational maneuver based on the informationincluded in the navigation information packet. The operations mayfurther include detecting a map origin change event. The operations mayalso include, in response to a detected map origin change event,determining an updated map origin and send to the one or more navigationsystem processors a notification indicative of a change from the initialmap origin to the updated map origin.

In an embodiment, a system may include at least one electronic horizonprocessor, which may include circuitry and a memory. The memory mayinclude instructions that when executed by the circuitry may cause theat least one electronic horizon processor to access a map representativeof at least a road segment on which the host vehicle travels or isexpected to travel. The map may include one or more splinesrepresentative of road features associated with the road segment. Theinstructions may also cause the at least one electronic horizonprocessor to receive an output provided by at least one vehicle sensor.The at least one vehicle sensor may include one or more camerasconfigured to capture images of an environment of the host vehicle. Thereceived output may include at least one image captured by the one ormore cameras. The instructions may also cause the at least oneelectronic horizon processor to localize the host vehicle relative to adrivable path for the host vehicle represented among the one or moresplines. The localization may be based on analysis of the at least oneimage captured by the one or more cameras. The instructions may furthercause the at least one electronic horizon processor to determine a setof points associated with the one or more splines based on thelocalization of the host vehicle relative to the drivable path for thehost vehicle. The instructions may also cause the at least oneelectronic horizon processor to generate a navigation information packetincluding information associated with the one or more splines and thedetermined set of points relative to the one or more splines. Theinstructions may further cause the at least one electronic horizonprocessor to output the generated navigation information packet to oneor more navigation system processors configured to cause the hostvehicle to execute at least one navigational maneuver based on theinformation included in the navigation information packet.

In an embodiment, a non-transitory computer readable medium may containinstructions that when executed by at least one electronic horizonprocessor, cause the at least one electronic horizon processor toperform operations including accessing a map representative of at leasta road segment on which a host vehicle travels or is expected to travel.The map may include one or more splines representative of road featuresassociated with the road segment and receiving an output provided by atleast one vehicle sensor. The at least one vehicle sensor may includeone or more cameras configured to capture images of an environment ofthe host vehicle, and the received output may include at least one imagecaptured by the one or more cameras. The operations may also includelocalizing the host vehicle relative to a drivable path for the hostvehicle represented among the one or more splines. The localization maybe based on analysis of the at least one image captured by the one ormore cameras. The operations may further include determining a set ofpoints associated with the one or more splines based on the localizationof the host vehicle relative to the drivable path for the host vehicle.The operations may also include generating a navigation informationpacket including information associated with the one or more splines andthe determined set of points relative to the one or more splines. Theoperations may further include outputting the generated navigationinformation packet to one or more navigation system processorsconfigured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 shows a sparse map for providing autonomous vehicle navigation,consistent with the disclosed embodiments.

FIG. 9A illustrates a polynomial representation of a portions of a roadsegment consistent with the disclosed embodiments.

FIG. 9B illustrates a curve in three-dimensional space representing atarget trajectory of a vehicle, for a particular road segment, includedin a sparse map consistent with the disclosed embodiments.

FIG. 10 illustrates example landmarks that may be included in sparse mapconsistent with the disclosed embodiments.

FIG. 11A shows polynomial representations of trajectories consistentwith the disclosed embodiments.

FIGS. 11B and 11C show target trajectories along a multi-lane roadconsistent with disclosed embodiments.

FIG. 11D shows an example road signature profile consistent withdisclosed embodiments.

FIG. 12 is a schematic illustration of a system that uses crowd sourcingdata received from a plurality of vehicles for autonomous vehiclenavigation, consistent with the disclosed embodiments.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines, consistent withthe disclosed embodiments.

FIG. 14 shows a map skeleton generated from combining locationinformation from many drives, consistent with the disclosed embodiments.

FIG. 15 shows an example of a longitudinal alignment of two drives withexample signs as landmarks, consistent with the disclosed embodiments.

FIG. 16 shows an example of a longitudinal alignment of many drives withan example sign as a landmark, consistent with the disclosedembodiments.

FIG. 17 is a schematic illustration of a system for generating drivedata using a camera, a vehicle, and a server, consistent with thedisclosed embodiments.

FIG. 18 is a schematic illustration of a system for crowdsourcing asparse map, consistent with the disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for generating asparse map for autonomous vehicle navigation along a road segment,consistent with the disclosed embodiments.

FIG. 20 illustrates a block diagram of a server consistent with thedisclosed embodiments.

FIG. 21 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles, consistent with the disclosed embodiments.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation, consistent with the disclosed embodiments.

FIGS. 24A, 24B, 24C, and 24D illustrate exemplary lane marks that may bedetected consistent with the disclosed embodiments.

FIG. 24E shows exemplary mapped lane marks consistent with the disclosedembodiments.

FIG. 24F shows an exemplary anomaly associated with detecting a lanemark consistent with the disclosed embodiments.

FIG. 25A shows an exemplary image of a vehicle's surrounding environmentfor navigation based on the mapped lane marks consistent with thedisclosed embodiments.

FIG. 25B illustrates a lateral localization correction of a vehiclebased on mapped lane marks in a road navigation model consistent withthe disclosed embodiments.

FIGS. 25C and 25D provide conceptual representations of a localizationtechnique for locating a host vehicle along a target trajectory usingmapped features included in a sparse map.

FIG. 26A is a flowchart showing an exemplary process for mapping a lanemark for use in autonomous vehicle navigation consistent with disclosedembodiments.

FIG. 26B is a flowchart showing an exemplary process for autonomouslynavigating a host vehicle along a road segment using mapped lane marksconsistent with disclosed embodiments.

FIG. 27 illustrates an exemplary system for providing one or more mapsegments to one or more vehicles, consistent with the disclosedembodiments.

FIGS. 28A, 28B, 28C, and 28D illustrate exemplary potential travelenvelopes for a vehicle, consistent with disclosed embodiments.

FIGS. 28E, 28F, 28G, and 28H illustrates exemplary map tiles associatedwith potential travel envelopes for a vehicle, consistent with disclosedembodiments.

FIGS. 29A and 29B illustrate exemplary map tiles, consistent withdisclosed embodiments.

FIG. 30 illustrates an exemplary process for retrieving map tiles,consistent with disclosed embodiments.

FIGS. 31A, 31B, 31C, and 31D illustrate an exemplary process fordecoding map tiles, consistent with disclosed embodiments.

FIG. 32 is a flowchart showing an exemplary process for providing one ormore map segments to one or more vehicles, consistent with the disclosedembodiments.

FIG. 33 is a schematic diagram that depicts the structure of a messagesent by the exemplary to a vehicle, consistent with disclosedembodiments.

FIG. 34 is an example of a message sent by the exemplary system to avehicle, consistent with disclosed embodiments.

FIG. 35 is a schematic diagram of exemplary electronic horizon map data,consistent with disclosed embodiments.

FIG. 36 is a schematic diagram of exemplary lane borders, consistentwith disclosed embodiments.

FIG. 37 illustrates an exemplary process for changing an electronichorizon coordinate system, consistent with disclosed embodiments.

FIG. 38 is a schematic diagram of exemplary indexes and sub-indexes ofsplines associated with road segments, consistent with disclosedembodiments.

FIG. 39 is a schematic diagram of an exemplary drivable path projectionof ten points per spline in set distances of a predetermined number ofmeters, consistent with disclosed embodiments.

FIG. 40 is a schematic diagram illustrating exemplary relationshipbetween the dynamic distance between points and the ego speed,consistent with disclosed embodiments.

FIG. 41 is a schematic diagram of exemplary drivable path merge points,consistent with disclosed embodiments.

FIG. 42 is a block diagram illustrating exemplary components of avehicle, consistent with disclosed embodiments.

FIG. 43 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments.

FIG. 44 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments.

FIG. 45 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments.

FIG. 46 is a schematic diagram illustrating exemplary predictedpositions of a vehicle at various time points, consistent with disclosedembodiments.

FIG. 47 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration of the vehicle. To beautonomous, a vehicle need not be fully automatic (e.g., fully operationwithout a driver or without driver input). Rather, an autonomous vehicleincludes those that can operate under driver control during certain timeperiods and without driver control during other time periods. Autonomousvehicles may also include vehicles that control only some aspects ofvehicle navigation, such as steering (e.g., to maintain a vehicle coursebetween vehicle lane constraints), but may leave other aspects to thedriver (e.g., braking). In some cases, autonomous vehicles may handlesome or all aspects of braking, speed control, and/or steering of thevehicle.

As human drivers typically rely on visual cues and observations tocontrol a vehicle, transportation infrastructures are built accordingly,with lane markings, traffic signs, and traffic lights are all designedto provide visual information to drivers. In view of these designcharacteristics of transportation infrastructures, an autonomous vehiclemay include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, components of the transportationinfrastructure (e.g., lane markings, traffic signs, traffic lights,etc.) that are observable by drivers and other obstacles (e.g., othervehicles, pedestrians, debris, etc.). Additionally, an autonomousvehicle may also use stored information, such as information thatprovides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while thevehicle is traveling, and the vehicle (as well as other vehicles) mayuse the information to localize itself on the model.

In some embodiments in this disclosure, an autonomous vehicle may useinformation obtained while navigating (e.g., from a camera, GPS device,an accelerometer, a speed sensor, a suspension sensor, etc.). In otherembodiments, an autonomous vehicle may use information obtained frompast navigations by the vehicle (or by other vehicles) while navigating.In yet other embodiments, an autonomous vehicle may use a combination ofinformation obtained while navigating and information obtained from pastnavigations. The following sections provide an overview of a systemconsistent with the disclosed embodiments, followed by an overview of aforward-facing imaging system and methods consistent with the system.The sections that follow disclose systems and methods for constructing,using, and updating a sparse map for autonomous vehicle navigation.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras), such as image capture device 122, image capture device124, and image capture device 126. System 100 may also include a datainterface 128 communicatively connecting processing device 110 to imageacquisition device 120. For example, data interface 128 may include anywired and/or wireless link or links for transmitting image data acquiredby image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), agraphics processing unit (GPU), a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc., orGPUs available from manufacturers such as NVIDIA®, ATI®, etc. and mayinclude various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be usedin the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. For example, processing devices such as field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and the like may be configured using, for example, one or more hardwaredescription languages (HDLs).

In other embodiments, configuring a processing device may includestoring executable instructions on a memory that is accessible to theprocessing device during operation. For example, the processing devicemay access the memory to obtain and execute the stored instructionsduring operation. In either case, the processing device configured toperform the sensing, image analysis, and/or navigational functionsdisclosed herein represents a specialized hardware-based system incontrol of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), a graphicsprocessing unit (GPU), support circuits, digital signal processors,integrated circuits, memory, or any other types of devices for imageprocessing and analysis. The image preprocessor may include a videoprocessor for capturing, digitizing and processing the imagery from theimage sensors. The CPU may comprise any number of microcontrollers ormicroprocessors. The GPU may also comprise any number ofmicrocontrollers or microprocessors. The support circuits may be anynumber of circuits generally well known in the art, including cache,power supply, clock and input-output circuits. The memory may storesoftware that, when executed by the processor, controls the operation ofthe system. The memory may include databases and image processingsoftware. The memory may comprise any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage and other types of storage. In one instance,the memory may be separate from the processing unit 110. In anotherinstance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory (RAM), read only memory (ROM), flash memory, disk drives,optical storage, tape storage, removable storage and/or any other typesof storage. In some embodiments, memory units 140, 150 may be separatefrom the applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a tachometer, a speedometer) for measuring a speed ofvehicle 200 and/or an accelerometer (either single axis or multiaxis)for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle.Systems and methods of generating such a map are discussed below withreferences to FIGS. 8-19.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from by system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead of transmitdata, such as captured images and/or limited location informationrelated to a route.

Other privacy levels are contemplated. For example, system 100 maytransmit data to a server according to an “intermediate” privacy leveland include additional information not included under a “high” privacylevel, such as a make and/or model of a vehicle and/or a vehicle type(e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In someembodiments, system 100 may upload data according to a “low” privacylevel. Under a “low” privacy level setting, system 100 may upload dataand include information sufficient to uniquely identify a specificvehicle, owner/driver, and/or a portion or entirely of a route traveledby the vehicle. Such “low” privacy level data may include one or moreof, for example, a VIN, a driver/owner name, an origination point of avehicle prior to departure, an intended destination of the vehicle, amake and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by dx, as shown in FIGS. 2C and 2D. In some embodiments, fore oraft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that the shield aligns against a vehicle windshieldhaving a matching slope. In some embodiments, each of image capturedevices 122, 124, and 126 may be positioned behind glare shield 380, asdepicted, for example, in FIG. 3D. The disclosed embodiments are notlimited to any particular configuration of image capture devices 122,124, and 126, camera mount 370, and glare shield 380. FIG. 3C is anillustration of camera mount 370 shown in FIG. 3B from a frontperspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122, 124, and 126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122, 124, and 126 may be positioned behindrearview mirror 310 and positioned substantially side-by-side (e.g., 6cm apart). Further, in some embodiments, as discussed above, one or moreof image capture devices 122, 124, and 126 may be mounted behind glareshield 380 that is flush with the windshield of vehicle 200. Suchshielding may act to minimize the impact of any reflections from insidethe car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402, 404, 406,and 408 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to application processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem, such as a system that may be configured to use computer visionalgorithms to detect and/or label objects in an environment from whichsensory information was captured and processed. In one embodiment,stereo image analysis module 404 and/or other image processing modulesmay be configured to use a combination of a trained and untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550, 552, 554, and 556, processing unit 110 mayidentify road marks appearing within the set of captured images andderive lane geometry information. Based on the identification and thederived information, processing unit 110 may cause one or morenavigational responses in vehicle 200, as described in connection withFIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560, 562, and 564, processing unit 110may identify traffic lights appearing within the set of captured imagesand analyze junction geometry information. Based on the identificationand the analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(i), z_(i))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(i)/z_(i)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Sparse Road Model for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may use a sparsemap for autonomous vehicle navigation. In particular, the sparse map maybe for autonomous vehicle navigation along a road segment. For example,the sparse map may provide sufficient information for navigating anautonomous vehicle without storing and/or updating a large quantity ofdata. As discussed below in further detail, an autonomous vehicle mayuse the sparse map to navigate one or more roads based on one or morestored trajectories.

Sparse Map for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, the sparsemap may provide sufficient information for navigation without requiringexcessive data storage or data transfer rates. As discussed below infurther detail, a vehicle (which may be an autonomous vehicle) may usethe sparse map to navigate one or more roads. For example, in someembodiments, the sparse map may include data related to a road andpotentially landmarks along the road that may be sufficient for vehiclenavigation, but which also exhibit small data footprints. For example,the sparse data maps described in detail below may require significantlyless storage space and data transfer bandwidth as compared with digitalmaps including detailed map information, such as image data collectedalong a road.

For example, rather than storing detailed representations of a roadsegment, the sparse data map may store three-dimensional polynomialrepresentations of preferred vehicle paths along a road. These paths mayrequire very little data storage space. Further, in the described sparsedata maps, landmarks may be identified and included in the sparse maproad model to aid in navigation. These landmarks may be located at anyspacing suitable for enabling vehicle navigation, but in some cases,such landmarks need not be identified and included in the model at highdensities and short spacings. Rather, in some cases, navigation may bepossible based on landmarks that are spaced apart by at least 50 meters,at least 100 meters, at least 500 meters, at least 1 kilometer, or atleast 2 kilometers. As will be discussed in more detail in othersections, the sparse map may be generated based on data collected ormeasured by vehicles equipped with various sensors and devices, such asimage capture devices, Global Positioning System sensors, motionsensors, etc., as the vehicles travel along roadways. In some cases, thesparse map may be generated based on data collected during multipledrives of one or more vehicles along a particular roadway. Generating asparse map using multiple drives of one or more vehicles may be referredto as “crowdsourcing” a sparse map.

Consistent with disclosed embodiments, an autonomous vehicle system mayuse a sparse map for navigation. For example, the disclosed systems andmethods may distribute a sparse map for generating a road navigationmodel for an autonomous vehicle and may navigate an autonomous vehiclealong a road segment using a sparse map and/or a generated roadnavigation model. Sparse maps consistent with the present disclosure mayinclude one or more three-dimensional contours that may representpredetermined trajectories that autonomous vehicles may traverse as theymove along associated road segments.

Sparse maps consistent with the present disclosure may also include datarepresenting one or more road features. Such road features may includerecognized landmarks, road signature profiles, and any otherroad-related features useful in navigating a vehicle. Sparse mapsconsistent with the present disclosure may enable autonomous navigationof a vehicle based on relatively small amounts of data included in thesparse map. For example, rather than including detailed representationsof a road, such as road edges, road curvature, images associated withroad segments, or data detailing other physical features associated witha road segment, the disclosed embodiments of the sparse map may requirerelatively little storage space (and relatively little bandwidth whenportions of the sparse map are transferred to a vehicle) but may stilladequately provide for autonomous vehicle navigation. The small datafootprint of the disclosed sparse maps, discussed in further detailbelow, may be achieved in some embodiments by storing representations ofroad-related elements that require small amounts of data but stillenable autonomous navigation.

For example, rather than storing detailed representations of variousaspects of a road, the disclosed sparse maps may store polynomialrepresentations of one or more trajectories that a vehicle may followalong the road. Thus, rather than storing (or having to transfer)details regarding the physical nature of the road to enable navigationalong the road, using the disclosed sparse maps, a vehicle may benavigated along a particular road segment without, in some cases, havingto interpret physical aspects of the road, but rather, by aligning itspath of travel with a trajectory (e.g., a polynomial spline) along theparticular road segment. In this way, the vehicle may be navigated basedmainly upon the stored trajectory (e.g., a polynomial spline) that mayrequire much less storage space than an approach involving storage ofroadway images, road parameters, road layout, etc.

In addition to the stored polynomial representations of trajectoriesalong a road segment, the disclosed sparse maps may also include smalldata objects that may represent a road feature. In some embodiments, thesmall data objects may include digital signatures, which are derivedfrom a digital image (or a digital signal) that was obtained by a sensor(e.g., a camera or other sensor, such as a suspension sensor) onboard avehicle traveling along the road segment. The digital signature may havea reduced size relative to the signal that was acquired by the sensor.In some embodiments, the digital signature may be created to becompatible with a classifier function that is configured to detect andto identify the road feature from the signal that is acquired by thesensor, for example, during a subsequent drive. In some embodiments, adigital signature may be created such that the digital signature has afootprint that is as small as possible, while retaining the ability tocorrelate or match the road feature with the stored signature based onan image (or a digital signal generated by a sensor, if the storedsignature is not based on an image and/or includes other data) of theroad feature that is captured by a camera onboard a vehicle travelingalong the same road segment at a subsequent time.

In some embodiments, a size of the data objects may be furtherassociated with a uniqueness of the road feature. For example, for aroad feature that is detectable by a camera onboard a vehicle, and wherethe camera system onboard the vehicle is coupled to a classifier that iscapable of distinguishing the image data corresponding to that roadfeature as being associated with a particular type of road feature, forexample, a road sign, and where such a road sign is locally unique inthat area (e.g., there is no identical road sign or road sign of thesame type nearby), it may be sufficient to store data indicating thetype of the road feature and its location.

As will be discussed in further detail below, road features (e.g.,landmarks along a road segment) may be stored as small data objects thatmay represent a road feature in relatively few bytes, while at the sametime providing sufficient information for recognizing and using such afeature for navigation. In one example, a road sign may be identified asa recognized landmark on which navigation of a vehicle may be based. Arepresentation of the road sign may be stored in the sparse map toinclude, e.g., a few bytes of data indicating a type of landmark (e.g.,a stop sign) and a few bytes of data indicating a location of thelandmark (e.g., coordinates). Navigating based on such data-lightrepresentations of the landmarks (e.g., using representations sufficientfor locating, recognizing, and navigating based upon the landmarks) mayprovide a desired level of navigational functionality associated withsparse maps without significantly increasing the data overheadassociated with the sparse maps. This lean representation of landmarks(and other road features) may take advantage of the sensors andprocessors included onboard such vehicles that are configured to detect,identify, and/or classify certain road features.

When, for example, a sign or even a particular type of a sign is locallyunique (e.g., when there is no other sign or no other sign of the sametype) in a given area, the sparse map may use data indicating a type ofa landmark (a sign or a specific type of sign), and during navigation(e.g., autonomous navigation) when a camera onboard an autonomousvehicle captures an image of the area including a sign (or of a specifictype of sign), the processor may process the image, detect the sign (ifindeed present in the image), classify the image as a sign (or as aspecific type of sign), and correlate the location of the image with thelocation of the sign as stored in the sparse map.

The sparse map may include any suitable representation of objectsidentified along a road segment. In some cases, the objects may bereferred to as semantic objects or non-semantic objects. Semanticobjects may include, for example, objects associated with apredetermined type classification. This type classification may beuseful in reducing the amount of data required to describe the semanticobject recognized in an environment, which can be beneficial both in theharvesting phase (e.g., to reduce costs associated with bandwidth usefor transferring drive information from a plurality of harvestingvehicles to a server) and during the navigation phase (e.g., reductionof map data can speed transfer of map tiles from a server to anavigating vehicle and can also reduce costs associated with bandwidthuse for such transfers). Semantic object classification types may beassigned to any type of objects or features that are expected to beencountered along a roadway.

Semantic objects may further be divided into two or more logical groups.For example, in some cases, one group of semantic object types may beassociated with predetermined dimensions. Such semantic objects mayinclude certain speed limit signs, yield signs, merge signs, stop signs,traffic lights, directional arrows on a roadway, manhole covers, or anyother type of object that may be associated with a standardized size.One benefit offered by such semantic objects is that very little datamay be needed to represent/fully define the objects. For example, if astandardized size of a speed limit size is known, then a harvestingvehicle may need only identify (through analysis of a captured image)the presence of a speed limit sign (a recognized type) along with anindication of a position of the detected speed limit sign (e.g., a 2Dposition in the captured image (or, alternatively, a 3D position in realworld coordinates) of a center of the sign or a certain corner of thesign) to provide sufficient information for map generation on the serverside. Where 2D image positions are transmitted to the server, a positionassociated with the captured image where the sign was detected may alsobe transmitted so the server can determine a real-world position of thesign (e.g., through structure in motion techniques using multiplecaptured images from one or more harvesting vehicles). Even with thislimited information (requiring just a few bytes to define each detectedobject), the server may construct the map including a fully representedspeed limit sign based on the type classification (representative of aspeed limit sign) received from one or more harvesting vehicles alongwith the position information for the detected sign.

Semantic objects may also include other recognized object or featuretypes that are not associated with certain standardized characteristics.Such objects or features may include potholes, tar seams, light poles,non-standardized signs, curbs, trees, tree branches, or any other typeof recognized object type with one or more variable characteristics(e.g., variable dimensions). In such cases, in addition to transmittingto a server an indication of the detected object or feature type (e.g.,pothole, pole, etc.) and position information for the detected object orfeature, a harvesting vehicle may also transmit an indication of a sizeof the object or feature. The size may be expressed in 2D imagedimensions (e.g., with a bounding box or one or more dimension values)or real-world dimensions (determined through structure in motioncalculations, based on LIDAR or RADAR system outputs, based on trainedneural network outputs, etc.).

Non-semantic objects or features may include any detectable objects orfeatures that fall outside of a recognized category or type, but thatstill may provide valuable information in map generation. In some cases,such non-semantic features may include a detected corner of a buildingor a corner of a detected window of a building, a unique stone or objectnear a roadway, a concrete splatter in a roadway shoulder, or any otherdetectable object or feature. Upon detecting such an object or featureone or more harvesting vehicles may transmit to a map generation servera location of one or more points (2D image points or 3D real worldpoints) associated with the detected object/feature. Additionally, acompressed or simplified image segment (e.g., an image hash) may begenerated for a region of the captured image including the detectedobject or feature. This image hash may be calculated based on apredetermined image processing algorithm and may form an effectivesignature for the detected non-semantic object or feature. Such asignature may be useful for navigation relative to a sparse mapincluding the non-semantic feature or object, as a vehicle traversingthe roadway may apply an algorithm similar to the algorithm used togenerate the image hash in order to confirm/verify the presence in acaptured image of the mapped non-semantic feature or object. Using thistechnique, non-semantic features may add to the richness of the sparsemaps (e.g., to enhance their usefulness in navigation) without addingsignificant data overhead.

As noted, target trajectories may be stored in the sparse map. Thesetarget trajectories (e.g., 3D splines) may represent the preferred orrecommended paths for each available lane of a roadway, each validpathway through a junction, for merges and exits, etc. In addition totarget trajectories, other road feature may also be detected, harvested,and incorporated in the sparse maps in the form of representativesplines. Such features may include, for example, road edges, lanemarkings, curbs, guardrails, or any other objects or features thatextend along a roadway or road segment.

Generating a Sparse Map

In some embodiments, a sparse map may include at least one linerepresentation of a road surface feature extending along a road segmentand a plurality of landmarks associated with the road segment. Incertain aspects, the sparse map may be generated via “crowdsourcing,”for example, through image analysis of a plurality of images acquired asone or more vehicles traverse the road segment.

FIG. 8 shows a sparse map 800 that one or more vehicles, e.g., vehicle200 (which may be an autonomous vehicle), may access for providingautonomous vehicle navigation. Sparse map 800 may be stored in a memory,such as memory 140 or 150. Such memory devices may include any types ofnon-transitory storage devices or computer-readable media. For example,in some embodiments, memory 140 or 150 may include hard drives, compactdiscs, flash memory, magnetic based memory devices, optical based memorydevices, etc. In some embodiments, sparse map 800 may be stored in adatabase (e.g., map database 160) that may be stored in memory 140 or150, or other types of storage devices.

In some embodiments, sparse map 800 may be stored on a storage device ora non-transitory computer-readable medium provided onboard vehicle 200(e.g., a storage device included in a navigation system onboard vehicle200). A processor (e.g., processing unit 110) provided on vehicle 200may access sparse map 800 stored in the storage device orcomputer-readable medium provided onboard vehicle 200 in order togenerate navigational instructions for guiding the autonomous vehicle200 as the vehicle traverses a road segment.

Sparse map 800 need not be stored locally with respect to a vehicle,however. In some embodiments, sparse map 800 may be stored on a storagedevice or computer-readable medium provided on a remote server thatcommunicates with vehicle 200 or a device associated with vehicle 200. Aprocessor (e.g., processing unit 110) provided on vehicle 200 mayreceive data included in sparse map 800 from the remote server and mayexecute the data for guiding the autonomous driving of vehicle 200. Insuch embodiments, the remote server may store all of sparse map 800 oronly a portion thereof. Accordingly, the storage device orcomputer-readable medium provided onboard vehicle 200 and/or onboard oneor more additional vehicles may store the remaining portion(s) of sparsemap 800.

Furthermore, in such embodiments, sparse map 800 may be made accessibleto a plurality of vehicles traversing various road segments (e.g., tens,hundreds, thousands, or millions of vehicles, etc.). It should be notedalso that sparse map 800 may include multiple sub-maps. For example, insome embodiments, sparse map 800 may include hundreds, thousands,millions, or more, of sub-maps (e.g., map tiles) that may be used innavigating a vehicle. Such sub-maps may be referred to as local maps ormap tiles, and a vehicle traveling along a roadway may access any numberof local maps relevant to a location in which the vehicle is traveling.The local map sections of sparse map 800 may be stored with a GlobalNavigation Satellite System (GNSS) key as an index to the database ofsparse map 800. Thus, while computation of steering angles fornavigating a host vehicle in the present system may be performed withoutreliance upon a GNSS position of the host vehicle, road features, orlandmarks, such GNSS information may be used for retrieval of relevantlocal maps.

In general, sparse map 800 may be generated based on data (e.g., driveinformation) collected from one or more vehicles as they travel alongroadways. For example, using sensors aboard the one or more vehicles(e.g., cameras, speedometers, GPS, accelerometers, etc.), thetrajectories that the one or more vehicles travel along a roadway may berecorded, and the polynomial representation of a preferred trajectoryfor vehicles making subsequent trips along the roadway may be determinedbased on the collected trajectories travelled by the one or morevehicles. Similarly, data collected by the one or more vehicles may aidin identifying potential landmarks along a particular roadway. Datacollected from traversing vehicles may also be used to identify roadprofile information, such as road width profiles, road roughnessprofiles, traffic line spacing profiles, road conditions, etc. Using thecollected information, sparse map 800 may be generated and distributed(e.g., for local storage or via on-the-fly data transmission) for use innavigating one or more autonomous vehicles. However, in someembodiments, map generation may not end upon initial generation of themap. As will be discussed in greater detail below, sparse map 800 may becontinuously or periodically updated based on data collected fromvehicles as those vehicles continue to traverse roadways included insparse map 800.

Data recorded in sparse map 800 may include position information basedon Global Positioning System (GPS) data. For example, locationinformation may be included in sparse map 800 for various map elements,including, for example, landmark locations, road profile locations, etc.Locations for map elements included in sparse map 800 may be obtainedusing GPS data collected from vehicles traversing a roadway. Forexample, a vehicle passing an identified landmark may determine alocation of the identified landmark using GPS position informationassociated with the vehicle and a determination of a location of theidentified landmark relative to the vehicle (e.g., based on imageanalysis of data collected from one or more cameras on board thevehicle). Such location determinations of an identified landmark (or anyother feature included in sparse map 800) may be repeated as additionalvehicles pass the location of the identified landmark. Some or all ofthe additional location determinations may be used to refine thelocation information stored in sparse map 800 relative to the identifiedlandmark. For example, in some embodiments, multiple positionmeasurements relative to a particular feature stored in sparse map 800may be averaged together. Any other mathematical operations, however,may also be used to refine a stored location of a map element based on aplurality of determined locations for the map element.

In a particular example, harvesting vehicles may traverse a particularroad segment. Each harvesting vehicle captures images of theirrespective environments. The images may be collected at any suitableframe capture rate (e.g., 9 Hz, etc.). Image analysis processor(s)aboard each harvesting vehicle analyze the captured images to detect thepresence of semantic and/or non-semantic features/objects. At a highlevel, the harvesting vehicles transmit to a mapping-server indicationsof detections of the semantic and/or non-semantic objects/features alongwith positions associated with those objects/features. In more detail,type indicators, dimension indicators, etc. may be transmitted togetherwith the position information. The position information may include anysuitable information for enabling the mapping server to aggregate thedetected objects/features into a sparse map useful in navigation. Insome cases, the position information may include one or more 2D imagepositions (e.g., X-Y pixel locations) in a captured image where thesemantic or non-semantic features/objects were detected. Such imagepositions may correspond to a center of the feature/object, a corner,etc. In this scenario, to aid the mapping server in reconstructing thedrive information and aligning the drive information from multipleharvesting vehicles, each harvesting vehicle may also provide the serverwith a location (e.g., a GPS location) where each image was captured.

In other cases, the harvesting vehicle may provide to the server one ormore 3D real world points associated with the detected objects/features.Such 3D points may be relative to a predetermined origin (such as anorigin of a drive segment) and may be determined through any suitabletechnique. In some cases, a structure in motion technique may be used todetermine the 3D real world position of a detected object/feature. Forexample, a certain object such as a particular speed limit sign may bedetected in two or more captured images. Using information such as theknown ego motion (speed, trajectory, GPS position, etc.) of theharvesting vehicle between the captured images, along with observedchanges of the speed limit sign in the captured images (change in X-Ypixel location, change in size, etc.), the real-world position of one ormore points associated with the speed limit sign may be determined andpassed along to the mapping server. Such an approach is optional, as itrequires more computation on the part of the harvesting vehicle systems.The sparse map of the disclosed embodiments may enable autonomousnavigation of a vehicle using relatively small amounts of stored data.In some embodiments, sparse map 800 may have a data density (e.g.,including data representing the target trajectories, landmarks, and anyother stored road features) of less than 2 MB per kilometer of roads,less than 1 MB per kilometer of roads, less than 500 kB per kilometer ofroads, or less than 100 kB per kilometer of roads. In some embodiments,the data density of sparse map 800 may be less than 10 kB per kilometerof roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB perkilometer), or no more than 10 kB per kilometer of roads, or no morethan 20 kB per kilometer of roads. In some embodiments, most, if notall, of the roadways of the United States may be navigated autonomouslyusing a sparse map having a total of 4 GB or less of data. These datadensity values may represent an average over an entire sparse map 800,over a local map within sparse map 800, and/or over a particular roadsegment within sparse map 800.

As noted, sparse map 800 may include representations of a plurality oftarget trajectories 810 for guiding autonomous driving or navigationalong a road segment. Such target trajectories may be stored asthree-dimensional splines. The target trajectories stored in sparse map800 may be determined based on two or more reconstructed trajectories ofprior traversals of vehicles along a particular road segment, forexample. A road segment may be associated with a single targettrajectory or multiple target trajectories. For example, on a two laneroad, a first target trajectory may be stored to represent an intendedpath of travel along the road in a first direction, and a second targettrajectory may be stored to represent an intended path of travel alongthe road in another direction (e.g., opposite to the first direction).Additional target trajectories may be stored with respect to aparticular road segment. For example, on a multi-lane road one or moretarget trajectories may be stored representing intended paths of travelfor vehicles in one or more lanes associated with the multi-lane road.In some embodiments, each lane of a multi-lane road may be associatedwith its own target trajectory. In other embodiments, there may be fewertarget trajectories stored than lanes present on a multi-lane road. Insuch cases, a vehicle navigating the multi-lane road may use any of thestored target trajectories to guides its navigation by taking intoaccount an amount of lane offset from a lane for which a targettrajectory is stored (e.g., if a vehicle is traveling in the left mostlane of a three lane highway, and a target trajectory is stored only forthe middle lane of the highway, the vehicle may navigate using thetarget trajectory of the middle lane by accounting for the amount oflane offset between the middle lane and the left-most lane whengenerating navigational instructions).

In some embodiments, the target trajectory may represent an ideal paththat a vehicle should take as the vehicle travels. The target trajectorymay be located, for example, at an approximate center of a lane oftravel. In other cases, the target trajectory may be located elsewhererelative to a road segment. For example, a target trajectory mayapproximately coincide with a center of a road, an edge of a road, or anedge of a lane, etc. In such cases, navigation based on the targettrajectory may include a determined amount of offset to be maintainedrelative to the location of the target trajectory. Moreover, in someembodiments, the determined amount of offset to be maintained relativeto the location of the target trajectory may differ based on a type ofvehicle (e.g., a passenger vehicle including two axles may have adifferent offset from a truck including more than two axles along atleast a portion of the target trajectory).

Sparse map 800 may also include data relating to a plurality ofpredetermined landmarks 820 associated with particular road segments,local maps, etc. As discussed in greater detail below, these landmarksmay be used in navigation of the autonomous vehicle. For example, insome embodiments, the landmarks may be used to determine a currentposition of the vehicle relative to a stored target trajectory. Withthis position information, the autonomous vehicle may be able to adjusta heading direction to match a direction of the target trajectory at thedetermined location.

The plurality of landmarks 820 may be identified and stored in sparsemap 800 at any suitable spacing. In some embodiments, landmarks may bestored at relatively high densities (e.g., every few meters or more). Insome embodiments, however, significantly larger landmark spacing valuesmay be employed. For example, in sparse map 800, identified (orrecognized) landmarks may be spaced apart by 10 meters, 20 meters, 50meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, theidentified landmarks may be located at distances of even more than 2kilometers apart.

Between landmarks, and therefore between determinations of vehicleposition relative to a target trajectory, the vehicle may navigate basedon dead reckoning in which the vehicle uses sensors to determine its egomotion and estimate its position relative to the target trajectory.Because errors may accumulate during navigation by dead reckoning, overtime the position determinations relative to the target trajectory maybecome increasingly less accurate. The vehicle may use landmarksoccurring in sparse map 800 (and their known locations) to remove thedead reckoning-induced errors in position determination. In this way,the identified landmarks included in sparse map 800 may serve asnavigational anchors from which an accurate position of the vehiclerelative to a target trajectory may be determined. Because a certainamount of error may be acceptable in position location, an identifiedlandmark need not always be available to an autonomous vehicle. Rather,suitable navigation may be possible even based on landmark spacings, asnoted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters,1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1identified landmark every 1 km of road may be sufficient to maintain alongitudinal position determination accuracy within 1 m. Thus, not everypotential landmark appearing along a road segment need be stored insparse map 800.

Moreover, in some embodiments, lane markings may be used forlocalization of the vehicle during landmark spacings. By using lanemarkings during landmark spacings, the accumulation of errors duringnavigation by dead reckoning may be minimized.

In addition to target trajectories and identified landmarks, sparse map800 may include information relating to various other road features. Forexample, FIG. 9A illustrates a representation of curves along aparticular road segment that may be stored in sparse map 800. In someembodiments, a single lane of a road may be modeled by athree-dimensional polynomial description of left and right sides of theroad. Such polynomials representing left and right sides of a singlelane are shown in FIG. 9A. Regardless of how many lanes a road may have,the road may be represented using polynomials in a way similar to thatillustrated in FIG. 9A. For example, left and right sides of amulti-lane road may be represented by polynomials similar to those shownin FIG. 9A, and intermediate lane markings included on a multi-lane road(e.g., dashed markings representing lane boundaries, solid yellow linesrepresenting boundaries between lanes traveling in different directions,etc.) may also be represented using polynomials such as those shown inFIG. 9A.

As shown in FIG. 9A, a lane 900 may be represented using polynomials(e.g., a first order, second order, third order, or any suitable orderpolynomials). For illustration, lane 900 is shown as a two-dimensionallane and the polynomials are shown as two-dimensional polynomials. Asdepicted in FIG. 9A, lane 900 includes a left side 910 and a right side920. In some embodiments, more than one polynomial may be used torepresent a location of each side of the road or lane boundary. Forexample, each of left side 910 and right side 920 may be represented bya plurality of polynomials of any suitable length. In some cases, thepolynomials may have a length of about 100 m, although other lengthsgreater than or less than 100 m may also be used. Additionally, thepolynomials can overlap with one another in order to facilitate seamlesstransitions in navigating based on subsequently encountered polynomialsas a host vehicle travels along a roadway. For example, each of leftside 910 and right side 920 may be represented by a plurality of thirdorder polynomials separated into segments of about 100 meters in length(an example of the first predetermined range), and overlapping eachother by about 50 meters. The polynomials representing the left side 910and the right side 920 may or may not have the same order. For example,in some embodiments, some polynomials may be second order polynomials,some may be third order polynomials, and some may be fourth orderpolynomials.

In the example shown in FIG. 9A, left side 910 of lane 900 isrepresented by two groups of third order polynomials. The first groupincludes polynomial segments 911, 912, and 913. The second groupincludes polynomial segments 914, 915, and 916. The two groups, whilesubstantially parallel to each other, follow the locations of theirrespective sides of the road. Polynomial segments 911, 912, 913, 914,915, and 916 have a length of about 100 meters and overlap adjacentsegments in the series by about 50 meters. As noted previously, however,polynomials of different lengths and different overlap amounts may alsobe used. For example, the polynomials may have lengths of 500 m, 1 km,or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m,or greater than 100 m. Additionally, while FIG. 9A is shown asrepresenting polynomials extending in 2D space (e.g., on the surface ofthe paper), it is to be understood that these polynomials may representcurves extending in three dimensions (e.g., including a heightcomponent) to represent elevation changes in a road segment in additionto X-Y curvature. In the example shown in FIG. 9A, right side 920 oflane 900 is further represented by a first group having polynomialsegments 921, 922, and 923 and a second group having polynomial segments924, 925, and 926.

Returning to the target trajectories of sparse map 800, FIG. 9B shows athree-dimensional polynomial representing a target trajectory for avehicle traveling along a particular road segment. The target trajectoryrepresents not only the X-Y path that a host vehicle should travel alonga particular road segment, but also the elevation change that the hostvehicle will experience when traveling along the road segment. Thus,each target trajectory in sparse map 800 may be represented by one ormore three-dimensional polynomials, like the three-dimensionalpolynomial 950 shown in FIG. 9B. Sparse map 800 may include a pluralityof trajectories (e.g., millions or billions or more to representtrajectories of vehicles along various road segments along roadwaysthroughout the world). In some embodiments, each target trajectory maycorrespond to a spline connecting three-dimensional polynomial segments.

Regarding the data footprint of polynomial curves stored in sparse map800, in some embodiments, each third degree polynomial may berepresented by four parameters, each requiring four bytes of data.Suitable representations may be obtained with third degree polynomialsrequiring about 192 bytes of data for every 100 m. This may translate toapproximately 200 kB per hour in data usage/transfer requirements for ahost vehicle traveling approximately 100 km/hr.

Sparse map 800 may describe the lanes network using a combination ofgeometry descriptors and meta-data. The geometry may be described bypolynomials or splines as described above. The meta-data may describethe number of lanes, special characteristics (such as a car pool lane),and possibly other sparse labels. The total footprint of such indicatorsmay be negligible.

Accordingly, a sparse map according to embodiments of the presentdisclosure may include at least one line representation of a roadsurface feature extending along the road segment, each linerepresentation representing a path along the road segment substantiallycorresponding with the road surface feature. In some embodiments, asdiscussed above, the at least one line representation of the roadsurface feature may include a spline, a polynomial representation, or acurve. Furthermore, in some embodiments, the road surface feature mayinclude at least one of a road edge or a lane marking. Moreover, asdiscussed below with respect to “crowdsourcing,” the road surfacefeature may be identified through image analysis of a plurality ofimages acquired as one or more vehicles traverse the road segment.

As previously noted, sparse map 800 may include a plurality ofpredetermined landmarks associated with a road segment. Rather thanstoring actual images of the landmarks and relying, for example, onimage recognition analysis based on captured images and stored images,each landmark in sparse map 800 may be represented and recognized usingless data than a stored, actual image would require. Data representinglandmarks may still include sufficient information for describing oridentifying the landmarks along a road. Storing data describingcharacteristics of landmarks, rather than the actual images oflandmarks, may reduce the size of sparse map 800.

FIG. 10 illustrates examples of types of landmarks that may berepresented in sparse map 800. The landmarks may include any visible andidentifiable objects along a road segment. The landmarks may be selectedsuch that they are fixed and do not change often with respect to theirlocations and/or content. The landmarks included in sparse map 800 maybe useful in determining a location of vehicle 200 with respect to atarget trajectory as the vehicle traverses a particular road segment.Examples of landmarks may include traffic signs, directional signs,general signs (e.g., rectangular signs), roadside fixtures (e.g.,lampposts, reflectors, etc.), and any other suitable category. In someembodiments, lane marks on the road, may also be included as landmarksin sparse map 800.

Examples of landmarks shown in FIG. 10 include traffic signs,directional signs, roadside fixtures, and general signs. Traffic signsmay include, for example, speed limit signs (e.g., speed limit sign1000), yield signs (e.g., yield sign 1005), route number signs (e.g.,route number sign 1010), traffic light signs (e.g., traffic light sign1015), stop signs (e.g., stop sign 1020). Directional signs may includea sign that includes one or more arrows indicating one or moredirections to different places. For example, directional signs mayinclude a highway sign 1025 having arrows for directing vehicles todifferent roads or places, an exit sign 1030 having an arrow directingvehicles off a road, etc. Accordingly, at least one of the plurality oflandmarks may include a road sign.

General signs may be unrelated to traffic. For example, general signsmay include billboards used for advertisement, or a welcome boardadjacent a border between two countries, states, counties, cities, ortowns. FIG. 10 shows a general sign 1040 (“Joe's Restaurant”). Althoughgeneral sign 1040 may have a rectangular shape, as shown in FIG. 10,general sign 1040 may have other shapes, such as square, circle,triangle, etc.

Landmarks may also include roadside fixtures. Roadside fixtures may beobjects that are not signs, and may not be related to traffic ordirections. For example, roadside fixtures may include lampposts (e.g.,lamppost 1035), power line posts, traffic light posts, etc.

Landmarks may also include beacons that may be specifically designed forusage in an autonomous vehicle navigation system. For example, suchbeacons may include stand-alone structures placed at predeterminedintervals to aid in navigating a host vehicle. Such beacons may alsoinclude visual/graphical information added to existing road signs (e.g.,icons, emblems, bar codes, etc.) that may be identified or recognized bya vehicle traveling along a road segment. Such beacons may also includeelectronic components. In such embodiments, electronic beacons (e.g.,RFID tags, etc.) may be used to transmit non-visual information to ahost vehicle. Such information may include, for example, landmarkidentification and/or landmark location information that a host vehiclemay use in determining its position along a target trajectory.

In some embodiments, the landmarks included in sparse map 800 may berepresented by a data object of a predetermined size. The datarepresenting a landmark may include any suitable parameters foridentifying a particular landmark. For example, in some embodiments,landmarks stored in sparse map 800 may include parameters such as aphysical size of the landmark (e.g., to support estimation of distanceto the landmark based on a known size/scale), a distance to a previouslandmark, lateral offset, height, a type code (e.g., a landmarktype-what type of directional sign, traffic sign, etc.), a GPScoordinate (e.g., to support global localization), and any othersuitable parameters. Each parameter may be associated with a data size.For example, a landmark size may be stored using 8 bytes of data. Adistance to a previous landmark, a lateral offset, and height may bespecified using 12 bytes of data. A type code associated with a landmarksuch as a directional sign or a traffic sign may require about 2 bytesof data. For general signs, an image signature enabling identificationof the general sign may be stored using 50 bytes of data storage. Thelandmark GPS position may be associated with 16 bytes of data storage.These data sizes for each parameter are examples only, and other datasizes may also be used. Representing landmarks in sparse map 800 in thismanner may offer a lean solution for efficiently representing landmarksin the database. In some embodiments, objects may be referred to asstandard semantic objects or non-standard semantic objects. A standardsemantic object may include any class of object for which there's astandardized set of characteristics (e.g., speed limit signs, warningsigns, directional signs, traffic lights, etc. having known dimensionsor other characteristics). A non-standard semantic object may includeany object that is not associated with a standardized set ofcharacteristics (e.g., general advertising signs, signs identifyingbusiness establishments, potholes, trees, etc. that may have variabledimensions). Each non-standard semantic object may be represented with38 bytes of data (e.g., 8 bytes for size; 12 bytes for distance toprevious landmark, lateral offset, and height; 2 bytes for a type code;and 16 bytes for position coordinates). Standard semantic objects may berepresented using even less data, as size information may not be neededby the mapping server to fully represent the object in the sparse map.

Sparse map 800 may use a tag system to represent landmark types. In somecases, each traffic sign or directional sign may be associated with itsown tag, which may be stored in the database as part of the landmarkidentification. For example, the database may include on the order of1000 different tags to represent various traffic signs and on the orderof about 10000 different tags to represent directional signs. Of course,any suitable number of tags may be used, and additional tags may becreated as needed. General purpose signs may be represented in someembodiments using less than about 100 bytes (e.g., about 86 bytesincluding 8 bytes for size; 12 bytes for distance to previous landmark,lateral offset, and height; 50 bytes for an image signature; and 16bytes for GPS coordinates).

Thus, for semantic road signs not requiring an image signature, the datadensity impact to sparse map 800, even at relatively high landmarkdensities of about 1 per 50 m, may be on the order of about 760 bytesper kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760bytes). Even for general purpose signs including an image signaturecomponent, the data density impact is about 1.72 kB per km (e.g., 20landmarks per km×86 bytes per landmark=1,720 bytes). For semantic roadsigns, this equates to about 76 kB per hour of data usage for a vehicletraveling 100 km/hr. For general purpose signs, this equates to about170 kB per hour for a vehicle traveling 100 km/hr. It should be notedthat in some environments (e.g., urban environments) there may be a muchhigher density of detected objects available for inclusion in the sparsemap (perhaps more than one per meter). In some embodiments, a generallyrectangular object, such as a rectangular sign, may be represented insparse map 800 by no more than 100 bytes of data. The representation ofthe generally rectangular object (e.g., general sign 1040) in sparse map800 may include a condensed image signature or image hash (e.g.,condensed image signature 1045) associated with the generallyrectangular object. This condensed image signature/image hash may bedetermined using any suitable image hashing algorithm and may be used,for example, to aid in identification of a general purpose sign, forexample, as a recognized landmark. Such a condensed image signature(e.g., image information derived from actual image data representing anobject) may avoid a need for storage of an actual image of an object ora need for comparative image analysis performed on actual images inorder to recognize landmarks.

Referring to FIG. 10, sparse map 800 may include or store a condensedimage signature 1045 associated with a general sign 1040, rather than anactual image of general sign 1040. For example, after an image capturedevice (e.g., image capture device 122, 124, or 126) captures an imageof general sign 1040, a processor (e.g., image processor 190 or anyother processor that can process images either aboard or remotelylocated relative to a host vehicle) may perform an image analysis toextract/create condensed image signature 1045 that includes a uniquesignature or pattern associated with general sign 1040. In oneembodiment, condensed image signature 1045 may include a shape, colorpattern, a brightness pattern, or any other feature that may beextracted from the image of general sign 1040 for describing generalsign 1040.

For example, in FIG. 10, the circles, triangles, and stars shown incondensed image signature 1045 may represent areas of different colors.The pattern represented by the circles, triangles, and stars may bestored in sparse map 800, e.g., within the 50 bytes designated toinclude an image signature. Notably, the circles, triangles, and starsare not necessarily meant to indicate that such shapes are stored aspart of the image signature. Rather, these shapes are meant toconceptually represent recognizable areas having discernible colordifferences, textual areas, graphical shapes, or other variations incharacteristics that may be associated with a general purpose sign. Suchcondensed image signatures can be used to identify a landmark in theform of a general sign. For example, the condensed image signature canbe used to perform a same-not-same analysis based on a comparison of astored condensed image signature with image data captured, for example,using a camera onboard an autonomous vehicle.

Accordingly, the plurality of landmarks may be identified through imageanalysis of the plurality of images acquired as one or more vehiclestraverse the road segment. As explained below with respect to“crowdsourcing,” in some embodiments, the image analysis to identify theplurality of landmarks may include accepting potential landmarks when aratio of images in which the landmark does appear to images in which thelandmark does not appear exceeds a threshold. Furthermore, in someembodiments, the image analysis to identify the plurality of landmarksmay include rejecting potential landmarks when a ratio of images inwhich the landmark does not appear to images in which the landmark doesappear exceeds a threshold.

Returning to the target trajectories a host vehicle may use to navigatea particular road segment, FIG. 11A shows polynomial representationstrajectories capturing during a process of building or maintainingsparse map 800. A polynomial representation of a target trajectoryincluded in sparse map 800 may be determined based on two or morereconstructed trajectories of prior traversals of vehicles along thesame road segment. In some embodiments, the polynomial representation ofthe target trajectory included in sparse map 800 may be an aggregationof two or more reconstructed trajectories of prior traversals ofvehicles along the same road segment. In some embodiments, thepolynomial representation of the target trajectory included in sparsemap 800 may be an average of the two or more reconstructed trajectoriesof prior traversals of vehicles along the same road segment. Othermathematical operations may also be used to construct a targettrajectory along a road path based on reconstructed trajectoriescollected from vehicles traversing along a road segment.

As shown in FIG. 11A, a road segment 1100 may be travelled by a numberof vehicles 200 at different times. Each vehicle 200 may collect datarelating to a path that the vehicle took along the road segment. Thepath traveled by a particular vehicle may be determined based on cameradata, accelerometer information, speed sensor information, and/or GPSinformation, among other potential sources. Such data may be used toreconstruct trajectories of vehicles traveling along the road segment,and based on these reconstructed trajectories, a target trajectory (ormultiple target trajectories) may be determined for the particular roadsegment. Such target trajectories may represent a preferred path of ahost vehicle (e.g., guided by an autonomous navigation system) as thevehicle travels along the road segment.

In the example shown in FIG. 11A, a first reconstructed trajectory 1101may be determined based on data received from a first vehicle traversingroad segment 1100 at a first time period (e.g., day 1), a secondreconstructed trajectory 1102 may be obtained from a second vehicletraversing road segment 1100 at a second time period (e.g., day 2), anda third reconstructed trajectory 1103 may be obtained from a thirdvehicle traversing road segment 1100 at a third time period (e.g., day3). Each trajectory 1101, 1102, and 1103 may be represented by apolynomial, such as a three-dimensional polynomial. It should be notedthat in some embodiments, any of the reconstructed trajectories may beassembled onboard the vehicles traversing road segment 1100.

Additionally, or alternatively, such reconstructed trajectories may bedetermined on a server side based on information received from vehiclestraversing road segment 1100. For example, in some embodiments, vehicles200 may transmit data to one or more servers relating to their motionalong road segment 1100 (e.g., steering angle, heading, time, position,speed, sensed road geometry, and/or sensed landmarks, among things). Theserver may reconstruct trajectories for vehicles 200 based on thereceived data. The server may also generate a target trajectory forguiding navigation of autonomous vehicle that will travel along the sameroad segment 1100 at a later time based on the first, second, and thirdtrajectories 1101, 1102, and 1103. While a target trajectory may beassociated with a single prior traversal of a road segment, in someembodiments, each target trajectory included in sparse map 800 may bedetermined based on two or more reconstructed trajectories of vehiclestraversing the same road segment. In FIG. 11A, the target trajectory isrepresented by 1110. In some embodiments, the target trajectory 1110 maybe generated based on an average of the first, second, and thirdtrajectories 1101, 1102, and 1103. In some embodiments, the targettrajectory 1110 included in sparse map 800 may be an aggregation (e.g.,a weighted combination) of two or more reconstructed trajectories.

At the mapping server, the server may receive actual trajectories for aparticular road segment from multiple harvesting vehicles traversing theroad segment. To generate a target trajectory for each valid path alongthe road segment (e.g., each lane, each drive direction, each paththrough a junction, etc.), the received actual trajectories may bealigned. The alignment process may include using detectedobjects/features identified along the road segment along with harvestedpositions of those detected objects/features to correlate the actual,harvested trajectories with one another. Once aligned, an average or“best fit” target trajectory for each available lane, etc. may bedetermined based on the aggregated, correlated/aligned actualtrajectories.

FIGS. 11B and 11C further illustrate the concept of target trajectoriesassociated with road segments present within a geographic region 1111.As shown in FIG. 11B, a first road segment 1120 within geographic region1111 may include a multilane road, which includes two lanes 1122designated for vehicle travel in a first direction and two additionallanes 1124 designated for vehicle travel in a second direction oppositeto the first direction. Lanes 1122 and lanes 1124 may be separated by adouble yellow line 1123. Geographic region 1111 may also include abranching road segment 1130 that intersects with road segment 1120. Roadsegment 1130 may include a two-lane road, each lane being designated fora different direction of travel. Geographic region 1111 may also includeother road features, such as a stop line 1132, a stop sign 1134, a speedlimit sign 1136, and a hazard sign 1138.

As shown in FIG. 11C, sparse map 800 may include a local map 1140including a road model for assisting with autonomous navigation ofvehicles within geographic region 1111. For example, local map 1140 mayinclude target trajectories for one or more lanes associated with roadsegments 1120 and/or 1130 within geographic region 1111. For example,local map 1140 may include target trajectories 1141 and/or 1142 that anautonomous vehicle may access or rely upon when traversing lanes 1122.Similarly, local map 1140 may include target trajectories 1143 and/or1144 that an autonomous vehicle may access or rely upon when traversinglanes 1124. Further, local map 1140 may include target trajectories 1145and/or 1146 that an autonomous vehicle may access or rely upon whentraversing road segment 1130. Target trajectory 1147 represents apreferred path an autonomous vehicle should follow when transitioningfrom lanes 1120 (and specifically, relative to target trajectory 1141associated with a right-most lane of lanes 1120) to road segment 1130(and specifically, relative to a target trajectory 1145 associated witha first side of road segment 1130. Similarly, target trajectory 1148represents a preferred path an autonomous vehicle should follow whentransitioning from road segment 1130 (and specifically, relative totarget trajectory 1146) to a portion of road segment 1124 (andspecifically, as shown, relative to a target trajectory 1143 associatedwith a left lane of lanes 1124.

Sparse map 800 may also include representations of other road-relatedfeatures associated with geographic region 1111. For example, sparse map800 may also include representations of one or more landmarks identifiedin geographic region 1111. Such landmarks may include a first landmark1150 associated with stop line 1132, a second landmark 1152 associatedwith stop sign 1134, a third landmark associated with speed limit sign1154, and a fourth landmark 1156 associated with hazard sign 1138. Suchlandmarks may be used, for example, to assist an autonomous vehicle indetermining its current location relative to any of the shown targettrajectories, such that the vehicle may adjust its heading to match adirection of the target trajectory at the determined location.

In some embodiments, sparse map 800 may also include road signatureprofiles. Such road signature profiles may be associated with anydiscernible/measurable variation in at least one parameter associatedwith a road. For example, in some cases, such profiles may be associatedwith variations in road surface information such as variations insurface roughness of a particular road segment, variations in road widthover a particular road segment, variations in distances between dashedlines painted along a particular road segment, variations in roadcurvature along a particular road segment, etc. FIG. 11D shows anexample of a road signature profile 1160. While profile 1160 mayrepresent any of the parameters mentioned above, or others, in oneexample, profile 1160 may represent a measure of road surface roughness,as obtained, for example, by monitoring one or more sensors providingoutputs indicative of an amount of suspension displacement as a vehicletravels a particular road segment.

Alternatively or concurrently, profile 1160 may represent variation inroad width, as determined based on image data obtained via a cameraonboard a vehicle traveling a particular road segment. Such profiles maybe useful, for example, in determining a particular location of anautonomous vehicle relative to a particular target trajectory. That is,as it traverses a road segment, an autonomous vehicle may measure aprofile associated with one or more parameters associated with the roadsegment. If the measured profile can be correlated/matched with apredetermined profile that plots the parameter variation with respect toposition along the road segment, then the measured and predeterminedprofiles may be used (e.g., by overlaying corresponding sections of themeasured and predetermined profiles) in order to determine a currentposition along the road segment and, therefore, a current positionrelative to a target trajectory for the road segment.

In some embodiments, sparse map 800 may include different trajectoriesbased on different characteristics associated with a user of autonomousvehicles, environmental conditions, and/or other parameters relating todriving. For example, in some embodiments, different trajectories may begenerated based on different user preferences and/or profiles. Sparsemap 800 including such different trajectories may be provided todifferent autonomous vehicles of different users. For example, someusers may prefer to avoid toll roads, while others may prefer to takethe shortest or fastest routes, regardless of whether there is a tollroad on the route. The disclosed systems may generate different sparsemaps with different trajectories based on such different userpreferences or profiles. As another example, some users may prefer totravel in a fast moving lane, while others may prefer to maintain aposition in the central lane at all times.

Different trajectories may be generated and included in sparse map 800based on different environmental conditions, such as day and night,snow, rain, fog, etc. Autonomous vehicles driving under differentenvironmental conditions may be provided with sparse map 800 generatedbased on such different environmental conditions. In some embodiments,cameras provided on autonomous vehicles may detect the environmentalconditions, and may provide such information back to a server thatgenerates and provides sparse maps. For example, the server may generateor update an already generated sparse map 800 to include trajectoriesthat may be more suitable or safer for autonomous driving under thedetected environmental conditions. The update of sparse map 800 based onenvironmental conditions may be performed dynamically as the autonomousvehicles are traveling along roads.

Other different parameters relating to driving may also be used as abasis for generating and providing different sparse maps to differentautonomous vehicles. For example, when an autonomous vehicle istraveling at a high speed, turns may be tighter. Trajectories associatedwith specific lanes, rather than roads, may be included in sparse map800 such that the autonomous vehicle may maintain within a specific laneas the vehicle follows a specific trajectory. When an image captured bya camera onboard the autonomous vehicle indicates that the vehicle hasdrifted outside of the lane (e.g., crossed the lane mark), an action maybe triggered within the vehicle to bring the vehicle back to thedesignated lane according to the specific trajectory.

Crowdsourcing a Sparse Map

The disclosed sparse maps may be efficiently (and passively) generatedthrough the power of crowdsourcing. For example, any private orcommercial vehicle equipped with a camera (e.g., a simple, lowresolution camera regularly included as OEM equipment on today'svehicles) and an appropriate image analysis processor can serve as aharvesting vehicle. No special equipment (e.g., high definition imagingand/or positioning systems) are required. As a result of the disclosedcrowdsourcing technique, the generated sparse maps may be extremelyaccurate and may include extremely refined position information(enabling navigation error limits of 10 cm or less) without requiringany specialized imaging or sensing equipment as input to the mapgeneration process. Crowdsourcing also enables much more rapid (andinexpensive) updates to the generated maps, as new drive information iscontinuously available to the mapping server system from any roadstraversed by private or commercial vehicles minimally equipped to alsoserve as harvesting vehicles. There is no need for designated vehiclesequipped with high-definition imaging and mapping sensors. Therefore,the expense associated with building such specialized vehicles can beavoided. Further, updates to the presently disclosed sparse maps may bemade much more rapidly than systems that rely upon dedicated,specialized mapping vehicles (which by virtue of their expense andspecial equipment are typically limited to a fleet of specializedvehicles of far lower numbers than the number of private or commercialvehicles already available for performing the disclosed harvestingtechniques).

The disclosed sparse maps generated through crowdsourcing may beextremely accurate because they may be generated based on many inputsfrom multiple (10s, hundreds, millions, etc.) of harvesting vehiclesthat have collected drive information along a particular road segment.For example, every harvesting vehicle that drives along a particularroad segment may record its actual trajectory and may determine positioninformation relative to detected objects/features along the roadsegment. This information is passed along from multiple harvestingvehicles to a server. The actual trajectories are aggregated to generatea refined, target trajectory for each valid drive path along the roadsegment. Additionally, the position information collected from themultiple harvesting vehicles for each of the detected objects/featuresalong the road segment (semantic or non-semantic) can also beaggregated. As a result, the mapped position of each detectedobject/feature may constitute an average of hundreds, thousands, ormillions of individually determined positions for each detectedobject/feature. Such a technique may yield extremely accurate mappedpositions for the detected objects/features.

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, disclosedsystems and methods may use crowdsourced data for generation of a sparsemap that one or more autonomous vehicles may use to navigate along asystem of roads. As used herein, “crowdsourcing” means that data arereceived from various vehicles (e.g., autonomous vehicles) travelling ona road segment at different times, and such data are used to generateand/or update the road model, including sparse map tiles. The model orany of its sparse map tiles may, in turn, be transmitted to the vehiclesor other vehicles later travelling along the road segment for assistingautonomous vehicle navigation. The road model may include a plurality oftarget trajectories representing preferred trajectories that autonomousvehicles should follow as they traverse a road segment. The targettrajectories may be the same as a reconstructed actual trajectorycollected from a vehicle traversing a road segment, which may betransmitted from the vehicle to a server. In some embodiments, thetarget trajectories may be different from actual trajectories that oneor more vehicles previously took when traversing a road segment. Thetarget trajectories may be generated based on actual trajectories (e.g.,through averaging or any other suitable operation).

The vehicle trajectory data that a vehicle may upload to a server maycorrespond with the actual reconstructed trajectory for the vehicle ormay correspond to a recommended trajectory, which may be based on orrelated to the actual reconstructed trajectory of the vehicle, but maydiffer from the actual reconstructed trajectory. For example, vehiclesmay modify their actual, reconstructed trajectories and submit (e.g.,recommend) to the server the modified actual trajectories. The roadmodel may use the recommended, modified trajectories as targettrajectories for autonomous navigation of other vehicles.

In addition to trajectory information, other information for potentialuse in building a sparse data map 800 may include information relatingto potential landmark candidates. For example, through crowd sourcing ofinformation, the disclosed systems and methods may identify potentiallandmarks in an environment and refine landmark positions. The landmarksmay be used by a navigation system of autonomous vehicles to determineand/or adjust the position of the vehicle along the target trajectories.

The reconstructed trajectories that a vehicle may generate as thevehicle travels along a road may be obtained by any suitable method. Insome embodiments, the reconstructed trajectories may be developed bystitching together segments of motion for the vehicle, using, e.g., egomotion estimation (e.g., three dimensional translation and threedimensional rotation of the camera, and hence the body of the vehicle).The rotation and translation estimation may be determined based onanalysis of images captured by one or more image capture devices alongwith information from other sensors or devices, such as inertial sensorsand speed sensors. For example, the inertial sensors may include anaccelerometer or other suitable sensors configured to measure changes intranslation and/or rotation of the vehicle body. The vehicle may includea speed sensor that measures a speed of the vehicle.

In some embodiments, the ego motion of the camera (and hence the vehiclebody) may be estimated based on an optical flow analysis of the capturedimages. An optical flow analysis of a sequence of images identifiesmovement of pixels from the sequence of images, and based on theidentified movement, determines motions of the vehicle. The ego motionmay be integrated over time and along the road segment to reconstruct atrajectory associated with the road segment that the vehicle hasfollowed.

Data (e.g., reconstructed trajectories) collected by multiple vehiclesin multiple drives along a road segment at different times may be usedto construct the road model (e.g., including the target trajectories,etc.) included in sparse data map 800. Data collected by multiplevehicles in multiple drives along a road segment at different times mayalso be averaged to increase an accuracy of the model. In someembodiments, data regarding the road geometry and/or landmarks may bereceived from multiple vehicles that travel through the common roadsegment at different times. Such data received from different vehiclesmay be combined to generate the road model and/or to update the roadmodel.

The geometry of a reconstructed trajectory (and also a targettrajectory) along a road segment may be represented by a curve in threedimensional space, which may be a spline connecting three dimensionalpolynomials. The reconstructed trajectory curve may be determined fromanalysis of a video stream or a plurality of images captured by a camerainstalled on the vehicle. In some embodiments, a location is identifiedin each frame or image that is a few meters ahead of the currentposition of the vehicle. This location is where the vehicle is expectedto travel to in a predetermined time period. This operation may berepeated frame by frame, and at the same time, the vehicle may computethe camera's ego motion (rotation and translation). At each frame orimage, a short range model for the desired path is generated by thevehicle in a reference frame that is attached to the camera. The shortrange models may be stitched together to obtain a three dimensionalmodel of the road in some coordinate frame, which may be an arbitrary orpredetermined coordinate frame. The three dimensional model of the roadmay then be fitted by a spline, which may include or connect one or morepolynomials of suitable orders.

To conclude the short range road model at each frame, one or moredetection modules may be used. For example, a bottom-up lane detectionmodule may be used. The bottom-up lane detection module may be usefulwhen lane marks are drawn on the road. This module may look for edges inthe image and assembles them together to form the lane marks. A secondmodule may be used together with the bottom-up lane detection module.The second module is an end-to-end deep neural network, which may betrained to predict the correct short range path from an input image. Inboth modules, the road model may be detected in the image coordinateframe and transformed to a three dimensional space that may be virtuallyattached to the camera.

Although the reconstructed trajectory modeling method may introduce anaccumulation of errors due to the integration of ego motion over a longperiod of time, which may include a noise component, such errors may beinconsequential as the generated model may provide sufficient accuracyfor navigation over a local scale. In addition, it is possible to cancelthe integrated error by using external sources of information, such assatellite images or geodetic measurements. For example, the disclosedsystems and methods may use a GNSS receiver to cancel accumulatederrors. However, the GNSS positioning signals may not be alwaysavailable and accurate. The disclosed systems and methods may enable asteering application that depends weakly on the availability andaccuracy of GNSS positioning. In such systems, the usage of the GNSSsignals may be limited. For example, in some embodiments, the disclosedsystems may use the GNSS signals for database indexing purposes only.

In some embodiments, the range scale (e.g., local scale) that may berelevant for an autonomous vehicle navigation steering application maybe on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc.Such distances may be used, as the geometrical road model is mainly usedfor two purposes: planning the trajectory ahead and localizing thevehicle on the road model. In some embodiments, the planning task mayuse the model over a typical range of 40 meters ahead (or any othersuitable distance ahead, such as 20 meters, 30 meters, 50 meters), whenthe control algorithm steers the vehicle according to a target pointlocated 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7seconds, 2 seconds, etc.). The localization task uses the road modelover a typical range of 60 meters behind the car (or any other suitabledistances, such as 50 meters, 100 meters, 150 meters, etc.), accordingto a method called “tail alignment” described in more detail in anothersection. The disclosed systems and methods may generate a geometricalmodel that has sufficient accuracy over particular range, such as 100meters, such that a planned trajectory will not deviate by more than,for example, 30 cm from the lane center.

As explained above, a three dimensional road model may be constructedfrom detecting short range sections and stitching them together. Thestitching may be enabled by computing a six degree ego motion model,using the videos and/or images captured by the camera, data from theinertial sensors that reflect the motions of the vehicle, and the hostvehicle velocity signal. The accumulated error may be small enough oversome local range scale, such as of the order of 100 meters. All this maybe completed in a single drive over a particular road segment.

In some embodiments, multiple drives may be used to average the resultedmodel, and to increase its accuracy further. The same car may travel thesame route multiple times, or multiple cars may send their collectedmodel data to a central server. In any case, a matching procedure may beperformed to identify overlapping models and to enable averaging inorder to generate target trajectories. The constructed model (e.g.,including the target trajectories) may be used for steering once aconvergence criterion is met. Subsequent drives may be used for furthermodel improvements and in order to accommodate infrastructure changes.

Sharing of driving experience (such as sensed data) between multiplecars becomes feasible if they are connected to a central server. Eachvehicle client may store a partial copy of a universal road model, whichmay be relevant for its current position. A bidirectional updateprocedure between the vehicles and the server may be performed by thevehicles and the server. The small footprint concept discussed aboveenables the disclosed systems and methods to perform the bidirectionalupdates using a very small bandwidth.

Information relating to potential landmarks may also be determined andforwarded to a central server. For example, the disclosed systems andmethods may determine one or more physical properties of a potentiallandmark based on one or more images that include the landmark. Thephysical properties may include a physical size (e.g., height, width) ofthe landmark, a distance from a vehicle to a landmark, a distancebetween the landmark to a previous landmark, the lateral position of thelandmark (e.g., the position of the landmark relative to the lane oftravel), the GPS coordinates of the landmark, a type of landmark,identification of text on the landmark, etc. For example, a vehicle mayanalyze one or more images captured by a camera to detect a potentiallandmark, such as a speed limit sign.

The vehicle may determine a distance from the vehicle to the landmark ora position associated with the landmark (e.g., any semantic ornon-semantic object or feature along a road segment) based on theanalysis of the one or more images. In some embodiments, the distancemay be determined based on analysis of images of the landmark using asuitable image analysis method, such as a scaling method and/or anoptical flow method. As previously noted, a position of theobject/feature may include a 2D image position (e.g., an X-Y pixelposition in one or more captured images) of one or more pointsassociated with the object/feature or may include a 3D real-worldposition of one or more points (e.g., determined through structure inmotion/optical flow techniques, LIDAR or RADAR information, etc.). Insome embodiments, the disclosed systems and methods may be configured todetermine a type or classification of a potential landmark. In case thevehicle determines that a certain potential landmark corresponds to apredetermined type or classification stored in a sparse map, it may besufficient for the vehicle to communicate to the server an indication ofthe type or classification of the landmark, along with its location. Theserver may store such indications. At a later time, during navigation, anavigating vehicle may capture an image that includes a representationof the landmark, process the image (e.g., using a classifier), andcompare the result landmark in order to confirm detection of the mappedlandmark and to use the mapped landmark in localizing the navigatingvehicle relative to the sparse map.

In some embodiments, multiple autonomous vehicles travelling on a roadsegment may communicate with a server. The vehicles (or clients) maygenerate a curve describing its drive (e.g., through ego motionintegration) in an arbitrary coordinate frame. The vehicles may detectlandmarks and locate them in the same frame. The vehicles may upload thecurve and the landmarks to the server. The server may collect data fromvehicles over multiple drives, and generate a unified road model. Forexample, as discussed below with respect to FIG. 19, the server maygenerate a sparse map having the unified road model using the uploadedcurves and landmarks.

The server may also distribute the model to clients (e.g., vehicles).For example, the server may distribute the sparse map to one or morevehicles. The server may continuously or periodically update the modelwhen receiving new data from the vehicles. For example, the server mayprocess the new data to evaluate whether the data includes informationthat should trigger an updated, or creation of new data on the server.The server may distribute the updated model or the updates to thevehicles for providing autonomous vehicle navigation.

The server may use one or more criteria for determining whether new datareceived from the vehicles should trigger an update to the model ortrigger creation of new data. For example, when the new data indicatesthat a previously recognized landmark at a specific location no longerexists, or is replaced by another landmark, the server may determinethat the new data should trigger an update to the model. As anotherexample, when the new data indicates that a road segment has beenclosed, and when this has been corroborated by data received from othervehicles, the server may determine that the new data should trigger anupdate to the model.

The server may distribute the updated model (or the updated portion ofthe model) to one or more vehicles that are traveling on the roadsegment, with which the updates to the model are associated. The servermay also distribute the updated model to vehicles that are about totravel on the road segment, or vehicles whose planned trip includes theroad segment, with which the updates to the model are associated. Forexample, while an autonomous vehicle is traveling along another roadsegment before reaching the road segment with which an update isassociated, the server may distribute the updates or updated model tothe autonomous vehicle before the vehicle reaches the road segment.

In some embodiments, the remote server may collect trajectories andlandmarks from multiple clients (e.g., vehicles that travel along acommon road segment). The server may match curves using landmarks andcreate an average road model based on the trajectories collected fromthe multiple vehicles. The server may also compute a graph of roads andthe most probable path at each node or conjunction of the road segment.For example, the remote server may align the trajectories to generate acrowdsourced sparse map from the collected trajectories.

The server may average landmark properties received from multiplevehicles that travelled along the common road segment, such as thedistances between one landmark to another (e.g., a previous one alongthe road segment) as measured by multiple vehicles, to determine anarc-length parameter and support localization along the path and speedcalibration for each client vehicle. The server may average the physicaldimensions of a landmark measured by multiple vehicles travelled alongthe common road segment and recognized the same landmark. The averagedphysical dimensions may be used to support distance estimation, such asthe distance from the vehicle to the landmark. The server may averagelateral positions of a landmark (e.g., position from the lane in whichvehicles are travelling in to the landmark) measured by multiplevehicles travelled along the common road segment and recognized the samelandmark. The averaged lateral portion may be used to support laneassignment. The server may average the GPS coordinates of the landmarkmeasured by multiple vehicles travelled along the same road segment andrecognized the same landmark. The averaged GPS coordinates of thelandmark may be used to support global localization or positioning ofthe landmark in the road model.

In some embodiments, the server may identify model changes, such asconstructions, detours, new signs, removal of signs, etc., based on datareceived from the vehicles. The server may continuously or periodicallyor instantaneously update the model upon receiving new data from thevehicles. The server may distribute updates to the model or the updatedmodel to vehicles for providing autonomous navigation. For example, asdiscussed further below, the server may use crowdsourced data to filterout “ghost” landmarks detected by vehicles.

In some embodiments, the server may analyze driver interventions duringthe autonomous driving. The server may analyze data received from thevehicle at the time and location where intervention occurs, and/or datareceived prior to the time the intervention occurred. The server mayidentify certain portions of the data that caused or are closely relatedto the intervention, for example, data indicating a temporary laneclosure setup, data indicating a pedestrian in the road. The server mayupdate the model based on the identified data. For example, the servermay modify one or more trajectories stored in the model.

FIG. 12 is a schematic illustration of a system that uses crowdsourcingto generate a sparse map (as well as distribute and navigate using acrowdsourced sparse map). FIG. 12 shows a road segment 1200 thatincludes one or more lanes. A plurality of vehicles 1205, 1210, 1215,1220, and 1225 may travel on road segment 1200 at the same time or atdifferent times (although shown as appearing on road segment 1200 at thesame time in FIG. 12). At least one of vehicles 1205, 1210, 1215, 1220,and 1225 may be an autonomous vehicle. For simplicity of the presentexample, all of the vehicles 1205, 1210, 1215, 1220, and 1225 arepresumed to be autonomous vehicles.

Each vehicle may be similar to vehicles disclosed in other embodiments(e.g., vehicle 200), and may include components or devices included inor associated with vehicles disclosed in other embodiments. Each vehiclemay be equipped with an image capture device or camera (e.g., imagecapture device 122 or camera 122). Each vehicle may communicate with aremote server 1230 via one or more networks (e.g., over a cellularnetwork and/or the Internet, etc.) through wireless communication paths1235, as indicated by the dashed lines. Each vehicle may transmit datato server 1230 and receive data from server 1230. For example, server1230 may collect data from multiple vehicles travelling on the roadsegment 1200 at different times, and may process the collected data togenerate an autonomous vehicle road navigation model, or an update tothe model. Server 1230 may transmit the autonomous vehicle roadnavigation model or the update to the model to the vehicles thattransmitted data to server 1230. Server 1230 may transmit the autonomousvehicle road navigation model or the update to the model to othervehicles that travel on road segment 1200 at later times.

As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment1200, navigation information collected (e.g., detected, sensed, ormeasured) by vehicles 1205, 1210, 1215, 1220, and 1225 may betransmitted to server 1230. In some embodiments, the navigationinformation may be associated with the common road segment 1200. Thenavigation information may include a trajectory associated with each ofthe vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travelsover road segment 1200. In some embodiments, the trajectory may bereconstructed based on data sensed by various sensors and devicesprovided on vehicle 1205. For example, the trajectory may bereconstructed based on at least one of accelerometer data, speed data,landmarks data, road geometry or profile data, vehicle positioning data,and ego motion data. In some embodiments, the trajectory may bereconstructed based on data from inertial sensors, such asaccelerometer, and the velocity of vehicle 1205 sensed by a speedsensor. In addition, in some embodiments, the trajectory may bedetermined (e.g., by a processor onboard each of vehicles 1205, 1210,1215, 1220, and 1225) based on sensed ego motion of the camera, whichmay indicate three dimensional translation and/or three dimensionalrotations (or rotational motions). The ego motion of the camera (andhence the vehicle body) may be determined from analysis of one or moreimages captured by the camera.

In some embodiments, the trajectory of vehicle 1205 may be determined bya processor provided aboard vehicle 1205 and transmitted to server 1230.In other embodiments, server 1230 may receive data sensed by the varioussensors and devices provided in vehicle 1205, and determine thetrajectory based on the data received from vehicle 1205.

In some embodiments, the navigation information transmitted fromvehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may includedata regarding the road surface, the road geometry, or the road profile.The geometry of road segment 1200 may include lane structure and/orlandmarks. The lane structure may include the total number of lanes ofroad segment 1200, the type of lanes (e.g., one-way lane, two-way lane,driving lane, passing lane, etc.), markings on lanes, width of lanes,etc. In some embodiments, the navigation information may include a laneassignment, e.g., which lane of a plurality of lanes a vehicle istraveling in. For example, the lane assignment may be associated with anumerical value “3” indicating that the vehicle is traveling on thethird lane from the left or right. As another example, the laneassignment may be associated with a text value “center lane” indicatingthe vehicle is traveling on the center lane.

Server 1230 may store the navigation information on a non-transitorycomputer-readable medium, such as a hard drive, a compact disc, a tape,a memory, etc. Server 1230 may generate (e.g., through a processorincluded in server 1230) at least a portion of an autonomous vehicleroad navigation model for the common road segment 1200 based on thenavigation information received from the plurality of vehicles 1205,1210, 1215, 1220, and 1225 and may store the model as a portion of asparse map. Server 1230 may determine a trajectory associated with eachlane based on crowdsourced data (e.g., navigation information) receivedfrom multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) thattravel on a lane of road segment at different times. Server 1230 maygenerate the autonomous vehicle road navigation model or a portion ofthe model (e.g., an updated portion) based on a plurality oftrajectories determined based on the crowd sourced navigation data.Server 1230 may transmit the model or the updated portion of the modelto one or more of autonomous vehicles 1205, 1210, 1215, 1220, and 1225traveling on road segment 1200 or any other autonomous vehicles thattravel on road segment at a later time for updating an existingautonomous vehicle road navigation model provided in a navigation systemof the vehicles. The autonomous vehicle road navigation model may beused by the autonomous vehicles in autonomously navigating along thecommon road segment 1200.

As explained above, the autonomous vehicle road navigation model may beincluded in a sparse map (e.g., sparse map 800 depicted in FIG. 8).Sparse map 800 may include sparse recording of data related to roadgeometry and/or landmarks along a road, which may provide sufficientinformation for guiding autonomous navigation of an autonomous vehicle,yet does not require excessive data storage. In some embodiments, theautonomous vehicle road navigation model may be stored separately fromsparse map 800, and may use map data from sparse map 800 when the modelis executed for navigation. In some embodiments, the autonomous vehicleroad navigation model may use map data included in sparse map 800 fordetermining target trajectories along road segment 1200 for guidingautonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and1225 or other vehicles that later travel along road segment 1200. Forexample, when the autonomous vehicle road navigation model is executedby a processor included in a navigation system of vehicle 1205, themodel may cause the processor to compare the trajectories determinedbased on the navigation information received from vehicle 1205 withpredetermined trajectories included in sparse map 800 to validate and/orcorrect the current traveling course of vehicle 1205.

In the autonomous vehicle road navigation model, the geometry of a roadfeature or target trajectory may be encoded by a curve in athree-dimensional space. In one embodiment, the curve may be a threedimensional spline including one or more connecting three dimensionalpolynomials. As one of skill in the art would understand, a spline maybe a numerical function that is piece-wise defined by a series ofpolynomials for fitting data. A spline for fitting the three dimensionalgeometry data of the road may include a linear spline (first order), aquadratic spline (second order), a cubic spline (third order), or anyother splines (other orders), or a combination thereof. The spline mayinclude one or more three dimensional polynomials of different ordersconnecting (e.g., fitting) data points of the three dimensional geometrydata of the road. In some embodiments, the autonomous vehicle roadnavigation model may include a three dimensional spline corresponding toa target trajectory along a common road segment (e.g., road segment1200) or a lane of the road segment 1200.

As explained above, the autonomous vehicle road navigation modelincluded in the sparse map may include other information, such asidentification of at least one landmark along road segment 1200. Thelandmark may be visible within a field of view of a camera (e.g., camera122) installed on each of vehicles 1205, 1210, 1215, 1220, and 1225. Insome embodiments, camera 122 may capture an image of a landmark. Aprocessor (e.g., processor 180, 190, or processing unit 110) provided onvehicle 1205 may process the image of the landmark to extractidentification information for the landmark. The landmark identificationinformation, rather than an actual image of the landmark, may be storedin sparse map 800. The landmark identification information may requiremuch less storage space than an actual image. Other sensors or systems(e.g., GPS system) may also provide certain identification informationof the landmark (e.g., position of landmark). The landmark may includeat least one of a traffic sign, an arrow marking, a lane marking, adashed lane marking, a traffic light, a stop line, a directional sign(e.g., a highway exit sign with an arrow indicating a direction, ahighway sign with arrows pointing to different directions or places), alandmark beacon, or a lamppost. A landmark beacon refers to a device(e.g., an RFID device) installed along a road segment that transmits orreflects a signal to a receiver installed on a vehicle, such that whenthe vehicle passes by the device, the beacon received by the vehicle andthe location of the device (e.g., determined from GPS location of thedevice) may be used as a landmark to be included in the autonomousvehicle road navigation model and/or the sparse map 800.

The identification of at least one landmark may include a position ofthe at least one landmark. The position of the landmark may bedetermined based on position measurements performed using sensor systems(e.g., Global Positioning Systems, inertial based positioning systems,landmark beacon, etc.) associated with the plurality of vehicles 1205,1210, 1215, 1220, and 1225. In some embodiments, the position of thelandmark may be determined by averaging the position measurementsdetected, collected, or received by sensor systems on different vehicles1205, 1210, 1215, 1220, and 1225 through multiple drives. For example,vehicles 1205, 1210, 1215, 1220, and 1225 may transmit positionmeasurements data to server 1230, which may average the positionmeasurements and use the averaged position measurement as the positionof the landmark. The position of the landmark may be continuouslyrefined by measurements received from vehicles in subsequent drives.

The identification of the landmark may include a size of the landmark.The processor provided on a vehicle (e.g., 1205) may estimate thephysical size of the landmark based on the analysis of the images.Server 1230 may receive multiple estimates of the physical size of thesame landmark from different vehicles over different drives. Server 1230may average the different estimates to arrive at a physical size for thelandmark, and store that landmark size in the road model. The physicalsize estimate may be used to further determine or estimate a distancefrom the vehicle to the landmark. The distance to the landmark may beestimated based on the current speed of the vehicle and a scale ofexpansion based on the position of the landmark appearing in the imagesrelative to the focus of expansion of the camera. For example, thedistance to landmark may be estimated by Z=V*dt*R/D, where V is thespeed of vehicle, R is the distance in the image from the landmark attime t1 to the focus of expansion, and D is the change in distance forthe landmark in the image from t1 to t2. dt represents the (t2−t1). Forexample, the distance to landmark may be estimated by Z=V*dt*R/D, whereV is the speed of vehicle, R is the distance in the image between thelandmark and the focus of expansion, dt is a time interval, and D is theimage displacement of the landmark along the epipolar line. Otherequations equivalent to the above equation, such as Z=V*ω/Δω, may beused for estimating the distance to the landmark. Here, V is the vehiclespeed, ω is an image length (like the object width), and Δω is thechange of that image length in a unit of time.

When the physical size of the landmark is known, the distance to thelandmark may also be determined based on the following equation:Z=f*W/ω, where f is the focal length, W is the size of the landmark(e.g., height or width), ω is the number of pixels when the landmarkleaves the image. From the above equation, a change in distance Z may becalculated using ΔZ=f*W*Δω/ω²+f*ΔW/ω, where ΔW decays to zero byaveraging, and where Δω is the number of pixels representing a boundingbox accuracy in the image. A value estimating the physical size of thelandmark may be calculated by averaging multiple observations at theserver side. The resulting error in distance estimation may be verysmall. There are two sources of error that may occur when using theformula above, namely ΔW and Δω). Their contribution to the distanceerror is given by ΔZ=f*W*Δω/ω²+f*ΔW/ω. However, ΔW decays to zero byaveraging; hence ΔZ is determined by Δω (e.g., the inaccuracy of thebounding box in the image).

For landmarks of unknown dimensions, the distance to the landmark may beestimated by tracking feature points on the landmark between successiveframes. For example, certain features appearing on a speed limit signmay be tracked between two or more image frames. Based on these trackedfeatures, a distance distribution per feature point may be generated.The distance estimate may be extracted from the distance distribution.For example, the most frequent distance appearing in the distancedistribution may be used as the distance estimate. As another example,the average of the distance distribution may be used as the distanceestimate.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines 1301, 1302, and1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are forillustration purpose only. Each spline may include one or more threedimensional polynomials connecting a plurality of data points 1310. Eachpolynomial may be a first order polynomial, a second order polynomial, athird order polynomial, or a combination of any suitable polynomialshaving different orders. Each data point 1310 may be associated with thenavigation information received from vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, each data point 1310 may be associatedwith data related to landmarks (e.g., size, location, and identificationinformation of landmarks) and/or road signature profiles (e.g., roadgeometry, road roughness profile, road curvature profile, road widthprofile). In some embodiments, some data points 1310 may be associatedwith data related to landmarks, and others may be associated with datarelated to road signature profiles.

FIG. 14 illustrates raw location data 1410 (e.g., GPS data) receivedfrom five separate drives. One drive may be separate from another driveif it was traversed by separate vehicles at the same time, by the samevehicle at separate times, or by separate vehicles at separate times. Toaccount for errors in the location data 1410 and for differing locationsof vehicles within the same lane (e.g., one vehicle may drive closer tothe left of a lane than another), server 1230 may generate a mapskeleton 1420 using one or more statistical techniques to determinewhether variations in the raw location data 1410 represent actualdivergences or statistical errors. Each path within skeleton 1420 may belinked back to the raw data 1410 that formed the path. For example, thepath between A and B within skeleton 1420 is linked to raw data 1410from drives 2, 3, 4, and 5 but not from drive 1. Skeleton 1420 may notbe detailed enough to be used to navigate a vehicle (e.g., because itcombines drives from multiple lanes on the same road unlike the splinesdescribed above) but may provide useful topological information and maybe used to define intersections.

FIG. 15 illustrates an example by which additional detail may begenerated for a sparse map within a segment of a map skeleton (e.g.,segment A to B within skeleton 1420). As depicted in FIG. 15, the data(e.g. ego-motion data, road markings data, and the like) may be shown asa function of position S (or S₁ or S₂) along the drive. Server 1230 mayidentify landmarks for the sparse map by identifying unique matchesbetween landmarks 1501, 1503, and 1505 of drive 1510 and landmarks 1507and 1509 of drive 1520. Such a matching algorithm may result inidentification of landmarks 1511, 1513, and 1515. One skilled in the artwould recognize, however, that other matching algorithms may be used.For example, probability optimization may be used in lieu of or incombination with unique matching. Server 1230 may longitudinally alignthe drives to align the matched landmarks. For example, server 1230 mayselect one drive (e.g., drive 1520) as a reference drive and then shiftand/or elastically stretch the other drive(s) (e.g., drive 1510) foralignment.

FIG. 16 shows an example of aligned landmark data for use in a sparsemap. In the example of FIG. 16, landmark 1610 comprises a road sign. Theexample of FIG. 16 further depicts data from a plurality of drives 1601,1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, thedata from drive 1613 consists of a “ghost” landmark, and the server 1230may identify it as such because none of drives 1601, 1603, 1605, 1607,1609, and 1611 include an identification of a landmark in the vicinityof the identified landmark in drive 1613. Accordingly, server 1230 mayaccept potential landmarks when a ratio of images in which the landmarkdoes appear to images in which the landmark does not appear exceeds athreshold and/or may reject potential landmarks when a ratio of imagesin which the landmark does not appear to images in which the landmarkdoes appear exceeds a threshold.

FIG. 17 depicts a system 1700 for generating drive data, which may beused to crowdsource a sparse map. As depicted in FIG. 17, system 1700may include a camera 1701 and a locating device 1703 (e.g., a GPSlocator). Camera 1701 and locating device 1703 may be mounted on avehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera1701 may produce a plurality of data of multiple types, e.g., ego motiondata, traffic sign data, road data, or the like. The camera data andlocation data may be segmented into drive segments 1705. For example,drive segments 1705 may each have camera data and location data fromless than 1 km of driving.

In some embodiments, system 1700 may remove redundancies in drivesegments 1705. For example, if a landmark appears in multiple imagesfrom camera 1701, system 1700 may strip the redundant data such that thedrive segments 1705 only contain one copy of the location of and anymetadata relating to the landmark. By way of further example, if a lanemarking appears in multiple images from camera 1701, system 1700 maystrip the redundant data such that the drive segments 1705 only containone copy of the location of and any metadata relating to the lanemarking.

System 1700 also includes a server (e.g., server 1230). Server 1230 mayreceive drive segments 1705 from the vehicle and recombine the drivesegments 1705 into a single drive 1707. Such an arrangement may allowfor reduce bandwidth requirements when transferring data between thevehicle and the server while also allowing for the server to store datarelating to an entire drive.

FIG. 18 depicts system 1700 of FIG. 17 further configured forcrowdsourcing a sparse map. As in FIG. 17, system 1700 includes vehicle1810, which captures drive data using, for example, a camera (whichproduces, e.g., ego motion data, traffic sign data, road data, or thelike) and a locating device (e.g., a GPS locator). As in FIG. 17,vehicle 1810 segments the collected data into drive segments (depictedas “DS1 1,” “DS2 1,” “DSN 1” in FIG. 18). Server 1230 then receives thedrive segments and reconstructs a drive (depicted as “Drive 1” in FIG.18) from the received segments.

As further depicted in FIG. 18, system 1700 also receives data fromadditional vehicles. For example, vehicle 1820 also captures drive datausing, for example, a camera (which produces, e.g., ego motion data,traffic sign data, road data, or the like) and a locating device (e.g.,a GPS locator). Similar to vehicle 1810, vehicle 1820 segments thecollected data into drive segments (depicted as “DS1 2,” “DS2 2,” “DSN2” in FIG. 18). Server 1230 then receives the drive segments andreconstructs a drive (depicted as “Drive 2” in FIG. 18) from thereceived segments. Any number of additional vehicles may be used. Forexample, FIG. 18 also includes “CAR N” that captures drive data,segments it into drive segments (depicted as “DS1 N,” “DS2 N,” “DSN N”in FIG. 18), and sends it to server 1230 for reconstruction into a drive(depicted as “Drive N” in FIG. 18).

As depicted in FIG. 18, server 1230 may construct a sparse map (depictedas “MAP”) using the reconstructed drives (e.g., “Drive 1,” “Drive 2,”and “Drive N”) collected from a plurality of vehicles (e.g., “CAR 1”(also labeled vehicle 1810), “CAR 2” (also labeled vehicle 1820), and“CAR N”).

FIG. 19 is a flowchart showing an example process 1900 for generating asparse map for autonomous vehicle navigation along a road segment.Process 1900 may be performed by one or more processing devices includedin server 1230.

Process 1900 may include receiving a plurality of images acquired as oneor more vehicles traverse the road segment (step 1905). Server 1230 mayreceive images from cameras included within one or more of vehicles1205, 1210, 1215, 1220, and 1225. For example, camera 122 may captureone or more images of the environment surrounding vehicle 1205 asvehicle 1205 travels along road segment 1200. In some embodiments,server 1230 may also receive stripped down image data that has hadredundancies removed by a processor on vehicle 1205, as discussed abovewith respect to FIG. 17.

Process 1900 may further include identifying, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment (step 1910). Each line representationmay represent a path along the road segment substantially correspondingwith the road surface feature. For example, server 1230 may analyze theenvironmental images received from camera 122 to identify a road edge ora lane marking and determine a trajectory of travel along road segment1200 associated with the road edge or lane marking. In some embodiments,the trajectory (or line representation) may include a spline, apolynomial representation, or a curve. Server 1230 may determine thetrajectory of travel of vehicle 1205 based on camera ego motions (e.g.,three dimensional translation and/or three dimensional rotationalmotions) received at step 1905.

Process 1900 may also include identifying, based on the plurality ofimages, a plurality of landmarks associated with the road segment (step1910). For example, server 1230 may analyze the environmental imagesreceived from camera 122 to identify one or more landmarks, such as roadsign along road segment 1200. Server 1230 may identify the landmarksusing analysis of the plurality of images acquired as one or morevehicles traverse the road segment. To enable crowdsourcing, theanalysis may include rules regarding accepting and rejecting possiblelandmarks associated with the road segment. For example, the analysismay include accepting potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold and/or rejecting potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.

Process 1900 may include other operations or steps performed by server1230. For example, the navigation information may include a targettrajectory for vehicles to travel along a road segment, and process 1900may include clustering, by server 1230, vehicle trajectories related tomultiple vehicles travelling on the road segment and determining thetarget trajectory based on the clustered vehicle trajectories, asdiscussed in further detail below. Clustering vehicle trajectories mayinclude clustering, by server 1230, the multiple trajectories related tothe vehicles travelling on the road segment into a plurality of clustersbased on at least one of the absolute heading of vehicles or laneassignment of the vehicles. Generating the target trajectory may includeaveraging, by server 1230, the clustered trajectories. By way of furtherexample, process 1900 may include aligning data received in step 1905.Other processes or steps performed by server 1230, as described above,may also be included in process 1900.

The disclosed systems and methods may include other features. Forexample, the disclosed systems may use local coordinates, rather thanglobal coordinates. For autonomous driving, some systems may presentdata in world coordinates. For example, longitude and latitudecoordinates on the earth surface may be used. In order to use the mapfor steering, the host vehicle may determine its position andorientation relative to the map. It seems natural to use a GPS device onboard, in order to position the vehicle on the map and in order to findthe rotation transformation between the body reference frame and theworld reference frame (e.g., North, East and Down). Once the bodyreference frame is aligned with the map reference frame, then thedesired route may be expressed in the body reference frame and thesteering commands may be computed or generated.

The disclosed systems and methods may enable autonomous vehiclenavigation (e.g., steering control) with low footprint models, which maybe collected by the autonomous vehicles themselves without the aid ofexpensive surveying equipment. To support the autonomous navigation(e.g., steering applications), the road model may include a sparse maphaving the geometry of the road, its lane structure, and landmarks thatmay be used to determine the location or position of vehicles along atrajectory included in the model. As discussed above, generation of thesparse map may be performed by a remote server that communicates withvehicles travelling on the road and that receives data from thevehicles. The data may include sensed data, trajectories reconstructedbased on the sensed data, and/or recommended trajectories that mayrepresent modified reconstructed trajectories. As discussed below, theserver may transmit the model back to the vehicles or other vehiclesthat later travel on the road to aid in autonomous navigation.

FIG. 20 illustrates a block diagram of server 1230. Server 1230 mayinclude a communication unit 2005, which may include both hardwarecomponents (e.g., communication control circuits, switches, andantenna), and software components (e.g., communication protocols,computer codes). For example, communication unit 2005 may include atleast one network interface. Server 1230 may communicate with vehicles1205, 1210, 1215, 1220, and 1225 through communication unit 2005. Forexample, server 1230 may receive, through communication unit 2005,navigation information transmitted from vehicles 1205, 1210, 1215, 1220,and 1225. Server 1230 may distribute, through communication unit 2005,the autonomous vehicle road navigation model to one or more autonomousvehicles.

Server 1230 may include at least one non-transitory storage medium 2010,such as a hard drive, a compact disc, a tape, etc. Storage device 1410may be configured to store data, such as navigation information receivedfrom vehicles 1205, 1210, 1215, 1220, and 1225 and/or the autonomousvehicle road navigation model that server 1230 generates based on thenavigation information. Storage device 2010 may be configured to storeany other information, such as a sparse map (e.g., sparse map 800discussed above with respect to FIG. 8).

In addition to or in place of storage device 2010, server 1230 mayinclude a memory 2015. Memory 2015 may be similar to or different frommemory 140 or 150. Memory 2015 may be a non-transitory memory, such as aflash memory, a random access memory, etc. Memory 2015 may be configuredto store data, such as computer codes or instructions executable by aprocessor (e.g., processor 2020), map data (e.g., data of sparse map800), the autonomous vehicle road navigation model, and/or navigationinformation received from vehicles 1205, 1210, 1215, 1220, and 1225.

Server 1230 may include at least one processing device 2020 configuredto execute computer codes or instructions stored in memory 2015 toperform various functions. For example, processing device 2020 mayanalyze the navigation information received from vehicles 1205, 1210,1215, 1220, and 1225, and generate the autonomous vehicle roadnavigation model based on the analysis. Processing device 2020 maycontrol communication unit 1405 to distribute the autonomous vehicleroad navigation model to one or more autonomous vehicles (e.g., one ormore of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle thattravels on road segment 1200 at a later time). Processing device 2020may be similar to or different from processor 180, 190, or processingunit 110.

FIG. 21 illustrates a block diagram of memory 2015, which may storecomputer code or instructions for performing one or more operations forgenerating a road navigation model for use in autonomous vehiclenavigation. As shown in FIG. 21, memory 2015 may store one or moremodules for performing the operations for processing vehicle navigationinformation. For example, memory 2015 may include a model generatingmodule 2105 and a model distributing module 2110. Processor 2020 mayexecute the instructions stored in any of modules 2105 and 2110 includedin memory 2015.

Model generating module 2105 may store instructions which, when executedby processor 2020, may generate at least a portion of an autonomousvehicle road navigation model for a common road segment (e.g., roadsegment 1200) based on navigation information received from vehicles1205, 1210, 1215, 1220, and 1225. For example, in generating theautonomous vehicle road navigation model, processor 2020 may clustervehicle trajectories along the common road segment 1200 into differentclusters. Processor 2020 may determine a target trajectory along thecommon road segment 1200 based on the clustered vehicle trajectories foreach of the different clusters. Such an operation may include finding amean or average trajectory of the clustered vehicle trajectories (e.g.,by averaging data representing the clustered vehicle trajectories) ineach cluster. In some embodiments, the target trajectory may beassociated with a single lane of the common road segment 1200.

The road model and/or sparse map may store trajectories associated witha road segment. These trajectories may be referred to as targettrajectories, which are provided to autonomous vehicles for autonomousnavigation. The target trajectories may be received from multiplevehicles, or may be generated based on actual trajectories orrecommended trajectories (actual trajectories with some modifications)received from multiple vehicles. The target trajectories included in theroad model or sparse map may be continuously updated (e.g., averaged)with new trajectories received from other vehicles.

Vehicles travelling on a road segment may collect data by varioussensors. The data may include landmarks, road signature profile, vehiclemotion (e.g., accelerometer data, speed data), vehicle position (e.g.,GPS data), and may either reconstruct the actual trajectoriesthemselves, or transmit the data to a server, which will reconstruct theactual trajectories for the vehicles. In some embodiments, the vehiclesmay transmit data relating to a trajectory (e.g., a curve in anarbitrary reference frame), landmarks data, and lane assignment alongtraveling path to server 1230. Various vehicles travelling along thesame road segment at multiple drives may have different trajectories.Server 1230 may identify routes or trajectories associated with eachlane from the trajectories received from vehicles through a clusteringprocess.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles 1205, 1210, 1215, 1220, and 1225 fordetermining a target trajectory for the common road segment (e.g., roadsegment 1200). The target trajectory or a plurality of targettrajectories determined from the clustering process may be included inthe autonomous vehicle road navigation model or sparse map 800. In someembodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling alongroad segment 1200 may transmit a plurality of trajectories 2200 toserver 1230. In some embodiments, server 1230 may generate trajectoriesbased on landmark, road geometry, and vehicle motion informationreceived from vehicles 1205, 1210, 1215, 1220, and 1225. To generate theautonomous vehicle road navigation model, server 1230 may clustervehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215,2220, 2225, and 2230, as shown in FIG. 22.

Clustering may be performed using various criteria. In some embodiments,all drives in a cluster may be similar with respect to the absoluteheading along the road segment 1200. The absolute heading may beobtained from GPS signals received by vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, the absolute heading may be obtainedusing dead reckoning. Dead reckoning, as one of skill in the art wouldunderstand, may be used to determine the current position and henceheading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previouslydetermined position, estimated speed, etc. Trajectories clustered byabsolute heading may be useful for identifying routes along theroadways.

In some embodiments, all the drives in a cluster may be similar withrespect to the lane assignment (e.g., in the same lane before and aftera junction) along the drive on road segment 1200. Trajectories clusteredby lane assignment may be useful for identifying lanes along theroadways. In some embodiments, both criteria (e.g., absolute heading andlane assignment) may be used for clustering.

In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories maybe averaged to obtain a target trajectory associated with the specificcluster. For example, the trajectories from multiple drives associatedwith the same lane cluster may be averaged. The averaged trajectory maybe a target trajectory associate with a specific lane. To average acluster of trajectories, server 1230 may select a reference frame of anarbitrary trajectory C0. For all other trajectories (C1, . . . , Cn),server 1230 may find a rigid transformation that maps Ci to C0, wherei=1, 2, . . . , n, where n is a positive integer number, correspondingto the total number of trajectories included in the cluster. Server 1230may compute a mean curve or trajectory in the C0 reference frame.

In some embodiments, the landmarks may define an arc length matchingbetween different drives, which may be used for alignment oftrajectories with lanes. In some embodiments, lane marks before andafter a junction may be used for alignment of trajectories with lanes.

To assemble lanes from the trajectories, server 1230 may select areference frame of an arbitrary lane. Server 1230 may map partiallyoverlapping lanes to the selected reference frame. Server 1230 maycontinue mapping until all lanes are in the same reference frame. Lanesthat are next to each other may be aligned as if they were the samelane, and later they may be shifted laterally.

Landmarks recognized along the road segment may be mapped to the commonreference frame, first at the lane level, then at the junction level.For example, the same landmarks may be recognized multiple times bymultiple vehicles in multiple drives. The data regarding the samelandmarks received in different drives may be slightly different. Suchdata may be averaged and mapped to the same reference frame, such as theC0 reference frame. Additionally or alternatively, the variance of thedata of the same landmark received in multiple drives may be calculated.

In some embodiments, each lane of road segment 120 may be associatedwith a target trajectory and certain landmarks. The target trajectory ora plurality of such target trajectories may be included in theautonomous vehicle road navigation model, which may be used later byother autonomous vehicles travelling along the same road segment 1200.Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 whilethe vehicles travel along road segment 1200 may be recorded inassociation with the target trajectory. The data of the targettrajectories and landmarks may be continuously or periodically updatedwith new data received from other vehicles in subsequent drives.

For localization of an autonomous vehicle, the disclosed systems andmethods may use an Extended Kalman Filter. The location of the vehiclemay be determined based on three dimensional position data and/or threedimensional orientation data, prediction of future location ahead ofvehicle's current location by integration of ego motion. Thelocalization of vehicle may be corrected or adjusted by imageobservations of landmarks. For example, when vehicle detects a landmarkwithin an image captured by the camera, the landmark may be compared toa known landmark stored within the road model or sparse map 800. Theknown landmark may have a known location (e.g., GPS data) along a targettrajectory stored in the road model and/or sparse map 800. Based on thecurrent speed and images of the landmark, the distance from the vehicleto the landmark may be estimated. The location of the vehicle along atarget trajectory may be adjusted based on the distance to the landmarkand the landmark's known location (stored in the road model or sparsemap 800). The landmark's position/location data (e.g., mean values frommultiple drives) stored in the road model and/or sparse map 800 may bepresumed to be accurate.

In some embodiments, the disclosed system may form a closed loopsubsystem, in which estimation of the vehicle six degrees of freedomlocation (e.g., three dimensional position data plus three dimensionalorientation data) may be used for navigating (e.g., steering the wheelof) the autonomous vehicle to reach a desired point (e.g., 1.3 secondahead in the stored). In turn, data measured from the steering andactual navigation may be used to estimate the six degrees of freedomlocation.

In some embodiments, poles along a road, such as lampposts and power orcable line poles may be used as landmarks for localizing the vehicles.Other landmarks such as traffic signs, traffic lights, arrows on theroad, stop lines, as well as static features or signatures of an objectalong the road segment may also be used as landmarks for localizing thevehicle. When poles are used for localization, the x observation of thepoles (i.e., the viewing angle from the vehicle) may be used, ratherthan the y observation (i.e., the distance to the pole) since thebottoms of the poles may be occluded and sometimes they are not on theroad plane.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation using a crowdsourced sparse map. Forillustration, the vehicle is referenced as vehicle 1205. The vehicleshown in FIG. 23 may be any other vehicle disclosed herein, including,for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200shown in other embodiments. As shown in FIG. 12, vehicle 1205 maycommunicate with server 1230. Vehicle 1205 may include an image capturedevice 122 (e.g., camera 122). Vehicle 1205 may include a navigationsystem 2300 configured for providing navigation guidance for vehicle1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 mayalso include other sensors, such as a speed sensor 2320 and anaccelerometer 2325. Speed sensor 2320 may be configured to detect thespeed of vehicle 1205. Accelerometer 2325 may be configured to detect anacceleration or deceleration of vehicle 1205. Vehicle 1205 shown in FIG.23 may be an autonomous vehicle, and the navigation system 2300 may beused for providing navigation guidance for autonomous driving.Alternatively, vehicle 1205 may also be a non-autonomous,human-controlled vehicle, and navigation system 2300 may still be usedfor providing navigation guidance.

Navigation system 2300 may include a communication unit 2305 configuredto communicate with server 1230 through communication path 1235.Navigation system 2300 may also include a GPS unit 2310 configured toreceive and process GPS signals. Navigation system 2300 may furtherinclude at least one processor 2315 configured to process data, such asGPS signals, map data from sparse map 800 (which may be stored on astorage device provided onboard vehicle 1205 and/or received from server1230), road geometry sensed by a road profile sensor 2330, imagescaptured by camera 122, and/or autonomous vehicle road navigation modelreceived from server 1230. The road profile sensor 2330 may includedifferent types of devices for measuring different types of roadprofile, such as road surface roughness, road width, road elevation,road curvature, etc. For example, the road profile sensor 2330 mayinclude a device that measures the motion of a suspension of vehicle2305 to derive the road roughness profile. In some embodiments, the roadprofile sensor 2330 may include radar sensors to measure the distancefrom vehicle 1205 to road sides (e.g., barrier on the road sides),thereby measuring the width of the road. In some embodiments, the roadprofile sensor 2330 may include a device configured for measuring the upand down elevation of the road. In some embodiment, the road profilesensor 2330 may include a device configured to measure the roadcurvature. For example, a camera (e.g., camera 122 or another camera)may be used to capture images of the road showing road curvatures.Vehicle 1205 may use such images to detect road curvatures.

The at least one processor 2315 may be programmed to receive, fromcamera 122, at least one environmental image associated with vehicle1205. The at least one processor 2315 may analyze the at least oneenvironmental image to determine navigation information related to thevehicle 1205. The navigation information may include a trajectoryrelated to the travel of vehicle 1205 along road segment 1200. The atleast one processor 2315 may determine the trajectory based on motionsof camera 122 (and hence the vehicle), such as three dimensionaltranslation and three dimensional rotational motions. In someembodiments, the at least one processor 2315 may determine thetranslation and rotational motions of camera 122 based on analysis of aplurality of images acquired by camera 122. In some embodiments, thenavigation information may include lane assignment information (e.g., inwhich lane vehicle 1205 is travelling along road segment 1200). Thenavigation information transmitted from vehicle 1205 to server 1230 maybe used by server 1230 to generate and/or update an autonomous vehicleroad navigation model, which may be transmitted back from server 1230 tovehicle 1205 for providing autonomous navigation guidance for vehicle1205.

The at least one processor 2315 may also be programmed to transmit thenavigation information from vehicle 1205 to server 1230. In someembodiments, the navigation information may be transmitted to server1230 along with road information. The road location information mayinclude at least one of the GPS signal received by the GPS unit 2310,landmark information, road geometry, lane information, etc. The at leastone processor 2315 may receive, from server 1230, the autonomous vehicleroad navigation model or a portion of the model. The autonomous vehicleroad navigation model received from server 1230 may include at least oneupdate based on the navigation information transmitted from vehicle 1205to server 1230. The portion of the model transmitted from server 1230 tovehicle 1205 may include an updated portion of the model. The at leastone processor 2315 may cause at least one navigational maneuver (e.g.,steering such as making a turn, braking, accelerating, passing anothervehicle, etc.) by vehicle 1205 based on the received autonomous vehicleroad navigation model or the updated portion of the model.

The at least one processor 2315 may be configured to communicate withvarious sensors and components included in vehicle 1205, includingcommunication unit 1705, GPS unit 2315, camera 122, speed sensor 2320,accelerometer 2325, and road profile sensor 2330. The at least oneprocessor 2315 may collect information or data from various sensors andcomponents, and transmit the information or data to server 1230 throughcommunication unit 2305. Alternatively or additionally, various sensorsor components of vehicle 1205 may also communicate with server 1230 andtransmit data or information collected by the sensors or components toserver 1230.

In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 maycommunicate with each other, and may share navigation information witheach other, such that at least one of the vehicles 1205, 1210, 1215,1220, and 1225 may generate the autonomous vehicle road navigation modelusing crowdsourcing, e.g., based on information shared by othervehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225may share navigation information with each other and each vehicle mayupdate its own the autonomous vehicle road navigation model provided inthe vehicle. In some embodiments, at least one of the vehicles 1205,1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hubvehicle. The at least one processor 2315 of the hub vehicle (e.g.,vehicle 1205) may perform some or all of the functions performed byserver 1230. For example, the at least one processor 2315 of the hubvehicle may communicate with other vehicles and receive navigationinformation from other vehicles. The at least one processor 2315 of thehub vehicle may generate the autonomous vehicle road navigation model oran update to the model based on the shared information received fromother vehicles. The at least one processor 2315 of the hub vehicle maytransmit the autonomous vehicle road navigation model or the update tothe model to other vehicles for providing autonomous navigationguidance.

Navigation Based on Sparse Maps

As previously discussed, the autonomous vehicle road navigation modelincluding sparse map 800 may include a plurality of mapped lane marksand a plurality of mapped objects/features associated with a roadsegment. As discussed in greater detail below, these mapped lane marks,objects, and features may be used when the autonomous vehicle navigates.For example, in some embodiments, the mapped objects and features may beused to localized a host vehicle relative to the map (e.g., relative toa mapped target trajectory). The mapped lane marks may be used (e.g., asa check) to determine a lateral position and/or orientation relative toa planned or target trajectory. With this position information, theautonomous vehicle may be able to adjust a heading direction to match adirection of a target trajectory at the determined position.

Vehicle 200 may be configured to detect lane marks in a given roadsegment. The road segment may include any markings on a road for guidingvehicle traffic on a roadway. For example, the lane marks may becontinuous or dashed lines demarking the edge of a lane of travel. Thelane marks may also include double lines, such as a double continuouslines, double dashed lines or a combination of continuous and dashedlines indicating, for example, whether passing is permitted in anadjacent lane. The lane marks may also include freeway entrance and exitmarkings indicating, for example, a deceleration lane for an exit rampor dotted lines indicating that a lane is turn-only or that the lane isending. The markings may further indicate a work zone, a temporary laneshift, a path of travel through an intersection, a median, a specialpurpose lane (e.g., a bike lane, HOV lane, etc.), or other miscellaneousmarkings (e.g., crosswalk, a speed hump, a railway crossing, a stopline, etc.).

Vehicle 200 may use cameras, such as image capture devices 122 and 124included in image acquisition unit 120, to capture images of thesurrounding lane marks. Vehicle 200 may analyze the images to detectpoint locations associated with the lane marks based on featuresidentified within one or more of the captured images. These pointlocations may be uploaded to a server to represent the lane marks insparse map 800. Depending on the position and field of view of thecamera, lane marks may be detected for both sides of the vehiclesimultaneously from a single image. In other embodiments, differentcameras may be used to capture images on multiple sides of the vehicle.Rather than uploading actual images of the lane marks, the marks may bestored in sparse map 800 as a spline or a series of points, thusreducing the size of sparse map 800 and/or the data that must beuploaded remotely by the vehicle.

FIGS. 24A-24D illustrate exemplary point locations that may be detectedby vehicle 200 to represent particular lane marks. Similar to thelandmarks described above, vehicle 200 may use various image recognitionalgorithms or software to identify point locations within a capturedimage. For example, vehicle 200 may recognize a series of edge points,corner points or various other point locations associated with aparticular lane mark. FIG. 24A shows a continuous lane mark 2410 thatmay be detected by vehicle 200. Lane mark 2410 may represent the outsideedge of a roadway, represented by a continuous white line. As shown inFIG. 24A, vehicle 200 may be configured to detect a plurality of edgelocation points 2411 along the lane mark. Location points 2411 may becollected to represent the lane mark at any intervals sufficient tocreate a mapped lane mark in the sparse map. For example, the lane markmay be represented by one point per meter of the detected edge, onepoint per every five meters of the detected edge, or at other suitablespacings. In some embodiments, the spacing may be determined by otherfactors, rather than at set intervals such as, for example, based onpoints where vehicle 200 has a highest confidence ranking of thelocation of the detected points. Although FIG. 24A shows edge locationpoints on an interior edge of lane mark 2410, points may be collected onthe outside edge of the line or along both edges. Further, while asingle line is shown in FIG. 24A, similar edge points may be detectedfor a double continuous line. For example, points 2411 may be detectedalong an edge of one or both of the continuous lines.

Vehicle 200 may also represent lane marks differently depending on thetype or shape of lane mark. FIG. 24B shows an exemplary dashed lane mark2420 that may be detected by vehicle 200. Rather than identifying edgepoints, as in FIG. 24A, vehicle may detect a series of corner points2421 representing corners of the lane dashes to define the full boundaryof the dash. While FIG. 24B shows each corner of a given dash markingbeing located, vehicle 200 may detect or upload a subset of the pointsshown in the figure. For example, vehicle 200 may detect the leadingedge or leading corner of a given dash mark, or may detect the twocorner points nearest the interior of the lane. Further, not every dashmark may be captured, for example, vehicle 200 may capture and/or recordpoints representing a sample of dash marks (e.g., every other, everythird, every fifth, etc.) or dash marks at a predefined spacing (e.g.,every meter, every five meters, every 10 meters, etc.) Corner points mayalso be detected for similar lane marks, such as markings showing a laneis for an exit ramp, that a particular lane is ending, or other variouslane marks that may have detectable corner points. Corner points mayalso be detected for lane marks consisting of double dashed lines or acombination of continuous and dashed lines.

In some embodiments, the points uploaded to the server to generate themapped lane marks may represent other points besides the detected edgepoints or corner points. FIG. 24C illustrates a series of points thatmay represent a centerline of a given lane mark. For example, continuouslane 2410 may be represented by centerline points 2441 along acenterline 2440 of the lane mark. In some embodiments, vehicle 200 maybe configured to detect these center points using various imagerecognition techniques, such as convolutional neural networks (CNN),scale-invariant feature transform (SIFT), histogram of orientedgradients (HOG) features, or other techniques. Alternatively, vehicle200 may detect other points, such as edge points 2411 shown in FIG. 24A,and may calculate centerline points 2441, for example, by detectingpoints along each edge and determining a midpoint between the edgepoints. Similarly, dashed lane mark 2420 may be represented bycenterline points 2451 along a centerline 2450 of the lane mark. Thecenterline points may be located at the edge of a dash, as shown in FIG.24C, or at various other locations along the centerline. For example,each dash may be represented by a single point in the geometric centerof the dash. The points may also be spaced at a predetermined intervalalong the centerline (e.g., every meter, 5 meters, 10 meters, etc.). Thecenterline points 2451 may be detected directly by vehicle 200, or maybe calculated based on other detected reference points, such as cornerpoints 2421, as shown in FIG. 24B. A centerline may also be used torepresent other lane mark types, such as a double line, using similartechniques as above.

In some embodiments, vehicle 200 may identify points representing otherfeatures, such as a vertex between two intersecting lane marks. FIG. 24Dshows exemplary points representing an intersection between two lanemarks 2460 and 2465. Vehicle 200 may calculate a vertex point 2466representing an intersection between the two lane marks. For example,one of lane marks 2460 or 2465 may represent a train crossing area orother crossing area in the road segment. While lane marks 2460 and 2465are shown as crossing each other perpendicularly, various otherconfigurations may be detected. For example, the lane marks 2460 and2465 may cross at other angles, or one or both of the lane marks mayterminate at the vertex point 2466. Similar techniques may also beapplied for intersections between dashed or other lane mark types. Inaddition to vertex point 2466, various other points 2467 may also bedetected, providing further information about the orientation of lanemarks 2460 and 2465.

Vehicle 200 may associate real-world coordinates with each detectedpoint of the lane mark. For example, location identifiers may begenerated, including coordinate for each point, to upload to a serverfor mapping the lane mark. The location identifiers may further includeother identifying information about the points, including whether thepoint represents a corner point, an edge point, center point, etc.Vehicle 200 may therefore be configured to determine a real-worldposition of each point based on analysis of the images. For example,vehicle 200 may detect other features in the image, such as the variouslandmarks described above, to locate the real-world position of the lanemarks. This may involve determining the location of the lane marks inthe image relative to the detected landmark or determining the positionof the vehicle based on the detected landmark and then determining adistance from the vehicle (or target trajectory of the vehicle) to thelane mark. When a landmark is not available, the location of the lanemark points may be determined relative to a position of the vehicledetermined based on dead reckoning. The real-world coordinates includedin the location identifiers may be represented as absolute coordinates(e.g., latitude/longitude coordinates), or may be relative to otherfeatures, such as based on a longitudinal position along a targettrajectory and a lateral distance from the target trajectory. Thelocation identifiers may then be uploaded to a server for generation ofthe mapped lane marks in the navigation model (such as sparse map 800).In some embodiments, the server may construct a spline representing thelane marks of a road segment. Alternatively, vehicle 200 may generatethe spline and upload it to the server to be recorded in thenavigational model.

FIG. 24E shows an exemplary navigation model or sparse map for acorresponding road segment that includes mapped lane marks. The sparsemap may include a target trajectory 2475 for a vehicle to follow along aroad segment. As described above, target trajectory 2475 may representan ideal path for a vehicle to take as it travels the corresponding roadsegment, or may be located elsewhere on the road (e.g., a centerline ofthe road, etc.). Target trajectory 2475 may be calculated in the variousmethods described above, for example, based on an aggregation (e.g., aweighted combination) of two or more reconstructed trajectories ofvehicles traversing the same road segment.

In some embodiments, the target trajectory may be generated equally forall vehicle types and for all road, vehicle, and/or environmentconditions. In other embodiments, however, various other factors orvariables may also be considered in generating the target trajectory. Adifferent target trajectory may be generated for different types ofvehicles (e.g., a private car, a light truck, and a full trailer). Forexample, a target trajectory with relatively tighter turning radii maybe generated for a small private car than a larger semi-trailer truck.In some embodiments, road, vehicle and environmental conditions may beconsidered as well. For example, a different target trajectory may begenerated for different road conditions (e.g., wet, snowy, icy, dry,etc.), vehicle conditions (e.g., tire condition or estimated tirecondition, brake condition or estimated brake condition, amount of fuelremaining, etc.) or environmental factors (e.g., time of day,visibility, weather, etc.). The target trajectory may also depend on oneor more aspects or features of a particular road segment (e.g., speedlimit, frequency and size of turns, grade, etc.). In some embodiments,various user settings may also be used to determine the targettrajectory, such as a set driving mode (e.g., desired drivingaggressiveness, economy mode, etc.).

The sparse map may also include mapped lane marks 2470 and 2480representing lane marks along the road segment. The mapped lane marksmay be represented by a plurality of location identifiers 2471 and 2481.As described above, the location identifiers may include locations inreal world coordinates of points associated with a detected lane mark.Similar to the target trajectory in the model, the lane marks may alsoinclude elevation data and may be represented as a curve inthree-dimensional space. For example, the curve may be a splineconnecting three dimensional polynomials of suitable order the curve maybe calculated based on the location identifiers. The mapped lane marksmay also include other information or metadata about the lane mark, suchas an identifier of the type of lane mark (e.g., between two lanes withthe same direction of travel, between two lanes of opposite direction oftravel, edge of a roadway, etc.) and/or other characteristics of thelane mark (e.g., continuous, dashed, single line, double line, yellow,white, etc.). In some embodiments, the mapped lane marks may becontinuously updated within the model, for example, using crowdsourcingtechniques. The same vehicle may upload location identifiers duringmultiple occasions of travelling the same road segment or data may beselected from a plurality of vehicles (such as 1205, 1210, 1215, 1220,and 1225) travelling the road segment at different times. Sparse map 800may then be updated or refined based on subsequent location identifiersreceived from the vehicles and stored in the system. As the mapped lanemarks are updated and refined, the updated road navigation model and/orsparse map may be distributed to a plurality of autonomous vehicles.

Generating the mapped lane marks in the sparse map may also includedetecting and/or mitigating errors based on anomalies in the images orin the actual lane marks themselves. FIG. 24F shows an exemplary anomaly2495 associated with detecting a lane mark 2490. Anomaly 2495 may appearin the image captured by vehicle 200, for example, from an objectobstructing the camera's view of the lane mark, debris on the lens, etc.In some instances, the anomaly may be due to the lane mark itself, whichmay be damaged or worn away, or partially covered, for example, by dirt,debris, water, snow or other materials on the road. Anomaly 2495 mayresult in an erroneous point 2491 being detected by vehicle 200. Sparsemap 800 may provide the correct the mapped lane mark and exclude theerror. In some embodiments, vehicle 200 may detect erroneous point 2491for example, by detecting anomaly 2495 in the image, or by identifyingthe error based on detected lane mark points before and after theanomaly. Based on detecting the anomaly, the vehicle may omit point 2491or may adjust it to be in line with other detected points. In otherembodiments, the error may be corrected after the point has beenuploaded, for example, by determining the point is outside of anexpected threshold based on other points uploaded during the same trip,or based on an aggregation of data from previous trips along the sameroad segment.

The mapped lane marks in the navigation model and/or sparse map may alsobe used for navigation by an autonomous vehicle traversing thecorresponding roadway. For example, a vehicle navigating along a targettrajectory may periodically use the mapped lane marks in the sparse mapto align itself with the target trajectory. As mentioned above, betweenlandmarks the vehicle may navigate based on dead reckoning in which thevehicle uses sensors to determine its ego motion and estimate itsposition relative to the target trajectory. Errors may accumulate overtime and vehicle's position determinations relative to the targettrajectory may become increasingly less accurate. Accordingly, thevehicle may use lane marks occurring in sparse map 800 (and their knownlocations) to reduce the dead reckoning-induced errors in positiondetermination. In this way, the identified lane marks included in sparsemap 800 may serve as navigational anchors from which an accurateposition of the vehicle relative to a target trajectory may bedetermined.

FIG. 25A shows an exemplary image 2500 of a vehicle's surroundingenvironment that may be used for navigation based on the mapped lanemarks. Image 2500 may be captured, for example, by vehicle 200 throughimage capture devices 122 and 124 included in image acquisition unit120. Image 2500 may include an image of at least one lane mark 2510, asshown in FIG. 25A. Image 2500 may also include one or more landmarks2521, such as road sign, used for navigation as described above. Someelements shown in FIG. 25A, such as elements 2511, 2530, and 2520 whichdo not appear in the captured image 2500 but are detected and/ordetermined by vehicle 200 are also shown for reference.

Using the various techniques described above with respect to FIGS. 24A-Dand 24F, a vehicle may analyze image 2500 to identify lane mark 2510.Various points 2511 may be detected corresponding to features of thelane mark in the image. Points 2511, for example, may correspond to anedge of the lane mark, a corner of the lane mark, a midpoint of the lanemark, a vertex between two intersecting lane marks, or various otherfeatures or locations. Points 2511 may be detected to correspond to alocation of points stored in a navigation model received from a server.For example, if a sparse map is received containing points thatrepresent a centerline of a mapped lane mark, points 2511 may also bedetected based on a centerline of lane mark 2510.

The vehicle may also determine a longitudinal position represented byelement 2520 and located along a target trajectory. Longitudinalposition 2520 may be determined from image 2500, for example, bydetecting landmark 2521 within image 2500 and comparing a measuredlocation to a known landmark location stored in the road model or sparsemap 800. The location of the vehicle along a target trajectory may thenbe determined based on the distance to the landmark and the landmark'sknown location. The longitudinal position 2520 may also be determinedfrom images other than those used to determine the position of a lanemark. For example, longitudinal position 2520 may be determined bydetecting landmarks in images from other cameras within imageacquisition unit 120 taken simultaneously or near simultaneously toimage 2500. In some instances, the vehicle may not be near any landmarksor other reference points for determining longitudinal position 2520. Insuch instances, the vehicle may be navigating based on dead reckoningand thus may use sensors to determine its ego motion and estimate alongitudinal position 2520 relative to the target trajectory. Thevehicle may also determine a distance 2530 representing the actualdistance between the vehicle and lane mark 2510 observed in the capturedimage(s). The camera angle, the speed of the vehicle, the width of thevehicle, or various other factors may be accounted for in determiningdistance 2530.

FIG. 25B illustrates a lateral localization correction of the vehiclebased on the mapped lane marks in a road navigation model. As describedabove, vehicle 200 may determine a distance 2530 between vehicle 200 anda lane mark 2510 using one or more images captured by vehicle 200.Vehicle 200 may also have access to a road navigation model, such assparse map 800, which may include a mapped lane mark 2550 and a targettrajectory 2555. Mapped lane mark 2550 may be modeled using thetechniques described above, for example using crowdsourced locationidentifiers captured by a plurality of vehicles. Target trajectory 2555may also be generated using the various techniques described previously.Vehicle 200 may also determine or estimate a longitudinal position 2520along target trajectory 2555 as described above with respect to FIG.25A. Vehicle 200 may then determine an expected distance 2540 based on alateral distance between target trajectory 2555 and mapped lane mark2550 corresponding to longitudinal position 2520. The laterallocalization of vehicle 200 may be corrected or adjusted by comparingthe actual distance 2530, measured using the captured image(s), with theexpected distance 2540 from the model.

FIGS. 25C and 25D provide illustrations associated with another examplefor localizing a host vehicle during navigation based on mappedlandmarks/objects/features in a sparse map. FIG. 25C conceptuallyrepresents a series of images captured from a vehicle navigating along aroad segment 2560. In this example, road segment 2560 includes astraight section of a two-lane divided highway delineated by road edges2561 and 2562 and center lane marking 2563. As shown, the host vehicleis navigating along a lane 2564, which is associated with a mappedtarget trajectory 2565. Thus, in an ideal situation (and withoutinfluencers such as the presence of target vehicles or objects in theroadway, etc.) the host vehicle should closely track the mapped targettrajectory 2565 as it navigates along lane 2564 of road segment 2560. Inreality, the host vehicle may experience drift as it navigates alongmapped target trajectory 2565. For effective and safe navigation, thisdrift should be maintained within acceptable limits (e.g., +/−10 cm oflateral displacement from target trajectory 2565 or any other suitablethreshold). To periodically account for drift and to make any neededcourse corrections to ensure that the host vehicle follows targettrajectory 2565, the disclosed navigation systems may be able tolocalize the host vehicle along the target trajectory 2565 (e.g.,determine a lateral and longitudinal position of the host vehiclerelative to the target trajectory 2565) using one or more mappedfeatures/objects included in the sparse map.

As a simple example, FIG. 25C shows a speed limit sign 2566 as it mayappear in five different, sequentially captured images as the hostvehicle navigates along road segment 2560. For example, at a first time,to, sign 2566 may appear in a captured image near the horizon. As thehost vehicle approaches sign 2566, in subsequentially captured images attimes t₁, t₂, t₃, and t₄, sign 2566 will appear at different 2D X-Ypixel locations of the captured images. For example, in the capturedimage space, sign 2566 will move downward and to the right along curve2567 (e.g., a curve extending through the center of the sign in each ofthe five captured image frames). Sign 2566 will also appear to increasein size as it is approached by the host vehicle (i.e., it will occupy agreat number of pixels in subsequently captured images).

These changes in the image space representations of an object, such assign 2566, may be exploited to determine a localized position of thehost vehicle along a target trajectory. For example, as described in thepresent disclosure, any detectable object or feature, such as a semanticfeature like sign 2566 or a detectable non-semantic feature, may beidentified by one or more harvesting vehicles that previously traverseda road segment (e.g., road segment 2560). A mapping server may collectthe harvested drive information from a plurality of vehicles, aggregateand correlate that information, and generate a sparse map including, forexample, a target trajectory 2565 for lane 2564 of road segment 2560.The sparse map may also store a location of sign 2566 (along with typeinformation, etc.). During navigation (e.g., prior to entering roadsegment 2560), a host vehicle may be supplied with a map tile includinga sparse map for road segment 2560. To navigate in lane 2564 of roadsegment 2560, the host vehicle may follow mapped target trajectory 2565.

The mapped representation of sign 2566 may be used by the host vehicleto localize itself relative to the target trajectory. For example, acamera on the host vehicle will capture an image 2570 of the environmentof the host vehicle, and that captured image 2570 may include an imagerepresentation of sign 2566 having a certain size and a certain X-Yimage location, as shown in FIG. 25D. This size and X-Y image locationcan be used to determine the host vehicle's position relative to targettrajectory 2565. For example, based on the sparse map including arepresentation of sign 2566, a navigation processor of the host vehiclecan determine that in response to the host vehicle traveling alongtarget trajectory 2565, a representation of sign 2566 should appear incaptured images such that a center of sign 2566 will move (in imagespace) along line 2567. If a captured image, such as image 2570, showsthe center (or other reference point) displaced from line 2567 (e.g.,the expected image space trajectory), then the host vehicle navigationsystem can determine that at the time of the captured image it was notlocated on target trajectory 2565. From the image, however, thenavigation processor can determine an appropriate navigationalcorrection to return the host vehicle to the target trajectory 2565. Forexample, if analysis shows an image location of sign 2566 that isdisplaced in the image by a distance 2572 to the left of the expectedimage space location on line 2567, then the navigation processor maycause a heading change by the host vehicle (e.g., change the steeringangle of the wheels) to move the host vehicle leftward by a distance2573. In this way, each captured image can be used as part of a feedbackloop process such that a difference between an observed image positionof sign 2566 and expected image trajectory 2567 may be minimized toensure that the host vehicle continues along target trajectory 2565 withlittle to no deviation. Of course, the more mapped objects that areavailable, the more often the described localization technique may beemployed, which can reduce or eliminate drift-induced deviations fromtarget trajectory 2565.

The process described above may be useful for detecting a lateralorientation or displacement of the host vehicle relative to a targettrajectory. Localization of the host vehicle relative to targettrajectory 2565 may also include a determination of a longitudinallocation of the target vehicle along the target trajectory. For example,captured image 2570 includes a representation of sign 2566 as having acertain image size (e.g., 2D X-Y pixel area). This size can be comparedto an expected image size of mapped sign 2566 as it travels throughimage space along line 2567 (e.g., as the size of the sign progressivelyincreases, as shown in FIG. 25C). Based on the image size of sign 2566in image 2570, and based on the expected size progression in image spacerelative to mapped target trajectory 2565, the host vehicle candetermine its longitudinal position (at the time when image 2570 wascaptured) relative to target trajectory 2565. This longitudinal positioncoupled with any lateral displacement relative to target trajectory2565, as described above, allows for full localization of the hostvehicle relative to target trajectory 2565, as the host vehiclenavigates along road 2560.

FIGS. 25C and 25D provide just one example of the disclosed localizationtechnique using a single mapped object and a single target trajectory.In other examples, there may be many more target trajectories (e.g., onetarget trajectory for each viable lane of a multi-lane highway, urbanstreet, complex junction, etc.) and there may be many more mappedavailable for localization. For example, a sparse map representative ofan urban environment may include many objects per meter available forlocalization.

FIG. 26A is a flowchart showing an exemplary process 2600A for mapping alane mark for use in autonomous vehicle navigation, consistent withdisclosed embodiments. At step 2610, process 2600A may include receivingtwo or more location identifiers associated with a detected lane mark.For example, step 2610 may be performed by server 1230 or one or moreprocessors associated with the server. The location identifiers mayinclude locations in real-world coordinates of points associated withthe detected lane mark, as described above with respect to FIG. 24E. Insome embodiments, the location identifiers may also contain other data,such as additional information about the road segment or the lane mark.Additional data may also be received during step 2610, such asaccelerometer data, speed data, landmarks data, road geometry or profiledata, vehicle positioning data, ego motion data, or various other formsof data described above. The location identifiers may be generated by avehicle, such as vehicles 1205, 1210, 1215, 1220, and 1225, based onimages captured by the vehicle. For example, the identifiers may bedetermined based on acquisition, from a camera associated with a hostvehicle, of at least one image representative of an environment of thehost vehicle, analysis of the at least one image to detect the lane markin the environment of the host vehicle, and analysis of the at least oneimage to determine a position of the detected lane mark relative to alocation associated with the host vehicle. As described above, the lanemark may include a variety of different marking types, and the locationidentifiers may correspond to a variety of points relative to the lanemark. For example, where the detected lane mark is part of a dashed linemarking a lane boundary, the points may correspond to detected cornersof the lane mark. Where the detected lane mark is part of a continuousline marking a lane boundary, the points may correspond to a detectededge of the lane mark, with various spacings as described above. In someembodiments, the points may correspond to the centerline of the detectedlane mark, as shown in FIG. 24C, or may correspond to a vertex betweentwo intersecting lane marks and at least one two other points associatedwith the intersecting lane marks, as shown in FIG. 24D.

At step 2612, process 2600A may include associating the detected lanemark with a corresponding road segment. For example, server 1230 mayanalyze the real-world coordinates, or other information received duringstep 2610, and compare the coordinates or other information to locationinformation stored in an autonomous vehicle road navigation model.Server 1230 may determine a road segment in the model that correspondsto the real-world road segment where the lane mark was detected.

At step 2614, process 2600A may include updating an autonomous vehicleroad navigation model relative to the corresponding road segment basedon the two or more location identifiers associated with the detectedlane mark. For example, the autonomous road navigation model may besparse map 800, and server 1230 may update the sparse map to include oradjust a mapped lane mark in the model. Server 1230 may update the modelbased on the various methods or processes described above with respectto FIG. 24E. In some embodiments, updating the autonomous vehicle roadnavigation model may include storing one or more indicators of positionin real world coordinates of the detected lane mark. The autonomousvehicle road navigation model may also include a at least one targettrajectory for a vehicle to follow along the corresponding road segment,as shown in FIG. 24E.

At step 2616, process 2600A may include distributing the updatedautonomous vehicle road navigation model to a plurality of autonomousvehicles. For example, server 1230 may distribute the updated autonomousvehicle road navigation model to vehicles 1205, 1210, 1215, 1220, and1225, which may use the model for navigation. The autonomous vehicleroad navigation model may be distributed via one or more networks (e.g.,over a cellular network and/or the Internet, etc.), through wirelesscommunication paths 1235, as shown in FIG. 12.

In some embodiments, the lane marks may be mapped using data receivedfrom a plurality of vehicles, such as through a crowdsourcing technique,as described above with respect to FIG. 24E. For example, process 2600Amay include receiving a first communication from a first host vehicle,including location identifiers associated with a detected lane mark, andreceiving a second communication from a second host vehicle, includingadditional location identifiers associated with the detected lane mark.For example, the second communication may be received from a subsequentvehicle travelling on the same road segment, or from the same vehicle ona subsequent trip along the same road segment. Process 2600A may furtherinclude refining a determination of at least one position associatedwith the detected lane mark based on the location identifiers receivedin the first communication and based on the additional locationidentifiers received in the second communication. This may include usingan average of the multiple location identifiers and/or filtering out“ghost” identifiers that may not reflect the real-world position of thelane mark.

FIG. 26B is a flowchart showing an exemplary process 2600B forautonomously navigating a host vehicle along a road segment using mappedlane marks. Process 2600B may be performed, for example, by processingunit 110 of autonomous vehicle 200. At step 2620, process 2600B mayinclude receiving from a server-based system an autonomous vehicle roadnavigation model. In some embodiments, the autonomous vehicle roadnavigation model may include a target trajectory for the host vehiclealong the road segment and location identifiers associated with one ormore lane marks associated with the road segment. For example, vehicle200 may receive sparse map 800 or another road navigation modeldeveloped using process 2600A. In some embodiments, the targettrajectory may be represented as a three-dimensional spline, forexample, as shown in FIG. 9B. As described above with respect to FIGS.24A-F, the location identifiers may include locations in real worldcoordinates of points associated with the lane mark (e.g., corner pointsof a dashed lane mark, edge points of a continuous lane mark, a vertexbetween two intersecting lane marks and other points associated with theintersecting lane marks, a centerline associated with the lane mark,etc.).

At step 2621, process 2600B may include receiving at least one imagerepresentative of an environment of the vehicle. The image may bereceived from an image capture device of the vehicle, such as throughimage capture devices 122 and 124 included in image acquisition unit120. The image may include an image of one or more lane marks, similarto image 2500 described above.

At step 2622, process 2600B may include determining a longitudinalposition of the host vehicle along the target trajectory. As describedabove with respect to FIG. 25A, this may be based on other informationin the captured image (e.g., landmarks, etc.) or by dead reckoning ofthe vehicle between detected landmarks.

At step 2623, process 2600B may include determining an expected lateraldistance to the lane mark based on the determined longitudinal positionof the host vehicle along the target trajectory and based on the two ormore location identifiers associated with the at least one lane mark.For example, vehicle 200 may use sparse map 800 to determine an expectedlateral distance to the lane mark. As shown in FIG. 25B, longitudinalposition 2520 along a target trajectory 2555 may be determined in step2622. Using spare map 800, vehicle 200 may determine an expecteddistance 2540 to mapped lane mark 2550 corresponding to longitudinalposition 2520.

At step 2624, process 2600B may include analyzing the at least one imageto identify the at least one lane mark. Vehicle 200, for example, mayuse various image recognition techniques or algorithms to identify thelane mark within the image, as described above. For example, lane mark2510 may be detected through image analysis of image 2500, as shown inFIG. 25A.

At step 2625, process 2600B may include determining an actual lateraldistance to the at least one lane mark based on analysis of the at leastone image. For example, the vehicle may determine a distance 2530, asshown in FIG. 25A, representing the actual distance between the vehicleand lane mark 2510. The camera angle, the speed of the vehicle, thewidth of the vehicle, the position of the camera relative to thevehicle, or various other factors may be accounted for in determiningdistance 2530.

At step 2626, process 2600B may include determining an autonomoussteering action for the host vehicle based on a difference between theexpected lateral distance to the at least one lane mark and thedetermined actual lateral distance to the at least one lane mark. Forexample, as described above with respect to FIG. 25B, vehicle 200 maycompare actual distance 2530 with an expected distance 2540. Thedifference between the actual and expected distance may indicate anerror (and its magnitude) between the vehicle's actual position and thetarget trajectory to be followed by the vehicle. Accordingly, thevehicle may determine an autonomous steering action or other autonomousaction based on the difference. For example, if actual distance 2530 isless than expected distance 2540, as shown in FIG. 25B, the vehicle maydetermine an autonomous steering action to direct the vehicle left, awayfrom lane mark 2510. Thus, the vehicle's position relative to the targettrajectory may be corrected. Process 2600B may be used, for example, toimprove navigation of the vehicle between landmarks.

Processes 2600A and 2600B provide examples only of techniques that maybe used for navigating a host vehicle using the disclosed sparse maps.In other examples, processes consistent with those described relative toFIGS. 25C and 25D may also be employed.

Map Management Using an Electronic Horizon

Although processing power and storage capacity has increased and becomeless costly, it may still be desirable to use them more efficiently. Thesystems and methods disclosed herein may allow a vehicle to dynamicallyreceive and load map data that is pertinent to its travel route, ratherthan loading a large set of map data that the vehicle may not use duringa trip. In doing so, the systems and methods may reduce hardwarerequirements of a vehicle by receiving and processing the map data thatthe vehicle is likely to need. Additionally, the systems and methods mayalso allow reduction in transmission costs of data exchanged between avehicle and, for example, a central server that deploys the map data.Furthermore, the disclosed systems and methods may allow the vehicle toreceive the most recent map data that the vehicle may need morefrequently. For example, the systems and methods may determine apotential travel area (or a potential travel envelope) for the vehiclebased on navigational information, such as the vehicle's location,speed, driving direction, etc. The systems and methods may also beconfigured to determine one or more road segments associated with thepotential travel area from the vehicle and transmit the map datarelating to the road segments to the vehicle. The vehicle (and/or thedriver) may navigate according to the received map data.

FIG. 27 illustrates an exemplary system 2700 for providing one or moremap segments to one or more vehicles, consistent with the disclosedembodiments. As illustrated in FIG. 27, system 2700 may include a server2701, one or more vehicles 2702, and one or more vehicle devices 2703associated with a vehicle, a database 2704, a network 2705, and aprocessing unit 2706 associated with one or more image capture devicesmountable on the vehicle. Server 2701 may be configured to providing oneor more map segments to one or more vehicles based on navigationalinformation received from one or more vehicles (and/or one or morevehicle devices associated with a vehicle). For example, processing unit2706 and/or vehicle device 2703 may be configured to collectnavigational information and transmit the navigational information toserver 2701. Server 2701 may send to processing unit 2706 and/or vehicledevice 2703 one or more map segments including map information for ageographical region based on the received navigational information.Database 2704 may be configured to store information for the componentsof system 2700 (e.g., server 2701, vehicle device 2703, and/orprocessing unit 2706). Network 2705 may be configured to facilitatecommunications among the components of system 2700.

Server 2701 may be configured to receive navigational information fromvehicle device 2703 and/or processing unit 2706. In some embodiments,navigational information may include the location of vehicle 2702, thespeed of vehicle 2702, and the direction of travel of vehicle 2702.Server 2701 may also be configured to analyze the received navigationalinformation and determine a potential travel envelope for vehicle 2702.A potential travel envelope of a vehicle may be an area surrounding thevehicle. For example, a potential travel envelope of a vehicle mayinclude an area covering a first predetermined distance from the vehiclein the driving direction of the vehicle, a second predetermined distancefrom the vehicle in a direction opposite to the driving direction of thevehicle, a third predetermined distance from the vehicle to the left ofthe vehicle, and a fourth predetermined distance from the vehicle to theright of the vehicle. In some embodiments, the first predetermineddistance from the vehicle in the driving direction of the vehicle mayinclude a predetermined distance ahead of the vehicle that mayconstitute an electronic horizon of the vehicle. In some embodiments, apotential travel envelope of a vehicle may include one or more distances(one, two, three, . . . , n), or any equivalents of distance (e.g., acertain time period given a speed parameter, etc.) from the vehicle inone or more (or all) possible driving directions for the vehiclerelative to its current position. In some embodiments, a potentialtravel envelope of a vehicle may include one or more distances (one,two, three, . . . , n) from the vehicle in one or more (or all) possibledriving directions for the vehicle relative to its current position, andin a direction where it is possible for the vehicle to drive relative toits current position (e.g., a driving direction may be included in thepotential travel envelope of the vehicle if the vehicle can drive inthat travel direction relative to its current position. For example, ina road where a vehicle may be capable of making a U-turn, a potentialtravel envelope of the vehicle may include a predetermined distance fromthe vehicle in the opposite direction in addition to a predetermineddistance in at least the forward direction, since the vehicle mayperform a U-turn and may navigate in a direction that is (generally)opposite to its current motion direction. As another example, iftraveling at the opposite direction is not possible (e.g., there is aphysical barrier) at the current location, and no U-turn is possible forsome distance ahead of the current location, a potent travel envelop maynot include a distance in the opposite direction. Thus, if the potentialtravel envelope of a vehicle is, for example, 1000 meters, and there isa U-turn starting at 800 meters ahead of the vehicle's current location,the potential travel envelope may include a distance of 1000 meters inthe current direction of travel of the vehicle and 200 meters in thereverse (after the U-turn) direction of travel, starting from 800 metersahead of the vehicle's current location (in its direction of travel).Similarly, if a U-turn is possible starting at 350 meters away from thecurrent location, and the potential travel envelope includes a distanceof 500 meters, the potential travel envelope may include a distance of500 meters in the current direction of travel of the vehicle and 150meters in the reverse (after the U-turn) direction of travel, startingfrom 350 meters ahead of the vehicle's current location (in itsdirection of travel). Multiple U-turns may be possible in different(possible) directions within the potential travel envelope, and for eachsuch U-turn, a distance in the travel direction after the U-turn may beincluded in the potential travel envelope. Similar to an actual horizonin the real world, the electronic horizon may be correlated to apotential travel distance of the vehicle within a certain timeparameter, such as a time window (30 seconds, 1 minutes, 3 minutes, 15minutes, 1 hour, 2 hours, etc.) and based on a speed parameters, such asbased on a current speed, an estimated speed, a typical speed for therelevant road segments included in the potential travel envelope of thehost vehicle and a current direction of travel. Server 2701 may furtherbe configured to send to vehicle device 2703 and/or processing unit 2706one or more map segments including map information for a geographicalregion at least partially overlapping with the potential travel envelopeof vehicle 2702.

In some embodiments, server 2701 may be a cloud server that performs thefunctions disclosed herein. The term “cloud server” refers to a computerplatform that provides services via a network, such as the Internet. Inthis example configuration, server 2701 may use virtual machines thatmay not correspond to individual hardware. For example, computationaland/or storage capabilities may be implemented by allocating appropriateportions of desirable computation/storage power from a scalablerepository, such as a data center or a distributed computingenvironment. In one example, server 2701 may implement the methodsdescribed herein using customized hard-wired logic, one or moreApplication Specific Integrated Circuits (ASICs) or Field ProgrammableGate Arrays (FPGAs), firmware, and/or program logic which, incombination with the computer system, cause server 2701 to be aspecial-purpose machine.

Processing unit 2706 and/or vehicle device 2703 may be configured tocollect navigational information and transmit the navigationalinformation to server 2701. For example, processing unit 2706 and/orvehicle device 2703 may be configured to receive data from one or moresensors and determine navigational information, such as the vehicle'slocation, speed, and/or driving direction, based on the received data.In some embodiments, navigational information may include sensor datareceived from one or more sensors associated with vehicle 2702 (e.g.,from a GPS device, a speed sensor, an accelerometer, a suspensionsensor, a camera, a LIDAR device, a Visual Detection and Ranging (VIDAR)device, or the like, or a combination thereof). Processing unit 2706and/or vehicle device 2703 may also be configured to transmit thenavigational information to server 2701 via, for example, network 2705.Alternatively or additionally, processing unit 2706 and/or vehicledevice 2703 may be configured to transmit sensor data to server 2701.Processing unit 2706 and/or vehicle device 2703 may also be configuredto receive map information from server 2701 via, for example, network2705. In one example, processing unit 2706 communicate with server 2701to obtain the map information and thereafter convey relevant mapinformation to vehicle device 2703. Map information may include datarelating to the position in a reference coordinate system of variousitems, including, for example, roads, water features, geographicfeatures, businesses, points of interest, restaurants, gas stations, asparse data model including polynomial representations of certain roadfeatures (e.g., lane markings), target trajectories for the hostvehicle, or the like, or a combination thereof. In some embodiments,processing unit 2706 and/or vehicle device 2703 may be configured toplan a routing path and/or navigate vehicle 2702 according to the mapinformation. For example, processing unit 2706 and/or vehicle device2703 may be configured to determine a route to a destination based onthe map information. Alternatively or additionally, processing unit 2706and/or vehicle device 2703 may be configured to determine at least onenavigational action (e.g., making a turn, stopping at a location, etc.)based on the received map information. In some embodiments, processingunit 2706 may include a device having a similar configuration and/orperforming similar functions as processing unit 110 described above.Alternatively or additionally, vehicle device 2703 may control orcommunicate with at least one of throttling system 220, braking system230, or steering system 240.

Database 2704 may include a map database configured to store map datafor the components of system 2700 (e.g., server 2701, processing unit2706, and/or vehicle device 2703). In some embodiments, server 2701,processing unit 2706, and/or vehicle device 2703 may be configured toaccess database 2704, and obtain data stored from and/or upload data todatabase 2704 via network 2705. For example, server 2701 may transmitdata relating to map information to database 2704 for storage.Processing unit 2706 and/or vehicle device 2703 may download mapinformation and/or data from database 2704. In some embodiments,database 2704 may include data relating to the position, in a referencecoordinate system, of various items, including roads, water features,geographic features, businesses, points of interest, restaurants, gasstations, or the like, or a combination thereof. In some embodiments,database 2704 may include a database similar to map database 160described elsewhere in this disclosure.

Network 2705 may be any type of network (including infrastructure) thatprovides communications, exchanges information, and/or facilitates theexchange of information between the components of system 2700. Forexample, network 2705 may include or be part of the Internet, a LocalArea Network, wireless network (e.g., a Wi-Fi/302.11 network), or othersuitable connections. In other embodiments, one or more components ofsystem 2700 may communicate directly through dedicated communicationlinks, such as, for example, a telephone network, an extranet, anintranet, the Internet, satellite communications, off-linecommunications, wireless communications, transponder communications, alocal area network (LAN), a wide area network (WAN), a virtual privatenetwork (VPN), and so forth.

As described elsewhere in this disclosure, processing unit 2706 maytransmit navigational information associated with vehicle 2702 to server2701 via network 2705. Server 2701 may analyze the navigationalinformation received from processing unit 2706 and determine a potentialtravel envelope for vehicle 2702 based on the analysis of thenavigational information. A potential travel envelop may encompass thelocation of vehicle 2702. In some embodiments, the potential travelenvelop may take on any shape (arbitrary shape) and is determined by adistance between two points in any possible driving direction relativeto the vehicle's current position and the vehicle's current travelingdirection. In some embodiments, a potential travel envelope may includea boundary. The boundary of the potential travel envelope may have ashape, including, for example, a triangular shape, a quadrilateralshape, a parallelogram shape, a rectangular shape, a square (orsubstantially square) shape, a trapezoid shape, a diamond shape, ahexagon shape, an octagon shape, a circular (or substantially circular)shape, an oval shape, an egg shape, an irregular shape, or the like, ora combination thereof. FIGS. 28A-28D illustrate exemplary potentialtravel envelopes for a vehicle in an area 2800, consistent withdisclosed embodiments. As illustrated in FIG. 28A, server 2701 maydetermine a potential travel envelope having a boundary 2811, which mayinclude a trapezoid shape, for vehicle 2702. As another example, asillustrated in FIG. 28B, server 2701 may determine a potential travelenvelope having a boundary 2812, which may include an oval shape, forvehicle 2702. As another example, as illustrated in FIG. 28C, server2701 may determine a potential travel envelope having a boundary 2813,which may include a triangle shape, for vehicle 2702. As anotherexample, as illustrated in FIG. 28D, server 2701 may determine apotential travel envelope having a boundary 2814, which may include arectangular shape, for vehicle 2702. Alternatively or additionally, theshape of the potential travel envelope may have a boundary that isdetermined by one or more potential paths that the vehicle may travel onstarting from a location (e.g., a current location) of the vehicle. Oneskilled in the art would understand that the shape of a potential travelenvelope is not limited to the exemplary shapes described in thisdisclosure. Other shapes are also possible. For example, a potentialtravel envelope may be associated with non-distance related constraints,such as countries, states, counties, cities, and/or roads), andend-of-the-road constraints. The potential travel envelope can includean irregular shape, for example, to accommodate such non-distancerelated constraints and/or a portion of any of the shapes describedherein.

As described elsewhere in this disclosure, server 2701 may also beconfigured to transmit to processing unit 2706 one or more map segmentsincluding map information for a geographical region at least partiallyoverlapping with the potential travel envelope of the vehicle. In someembodiments, the one or more map segments sent to processing unit 2706and/or vehicle device 2703 may include one or more tiles representing aregion of a predetermined dimension. The size and/or shape of a tile mayvary. In some embodiments, the dimension of a tile in a region may be ina range of 0.25 to 100 square kilometers, which may be restricted into asubrange of 0.25 square kilometers to 1 square kilometer, 1 squarekilometers to 10 square kilometers, and 10 to 25 square kilometers, 25to 50 square kilometers, and 50 to 100 square kilometers. In someembodiments, the predetermined dimension of the tile(s) may be less thanor equal to ten square kilometers. Alternatively, the predetermineddimension of the tile(s) may be less than or equal to one squarekilometer. Alternatively, the predetermined dimension of the tile(s) maybe less than or equal to ten square kilometers. Alternatively oradditionally, the size of a tile may vary based on the type of theregion in which a tile is. For example, the size of a tile in a ruralarea or an area with fewer roads than may be larger than the size of atile in an urban area or area with more roads. In some embodiments, thesize of the tile may be determined based on the density of theinformation around a current location of the vehicle (or the locationwhere the data is obtained), possible paths for the vehicle, the numberof possible routes in the area, the type of routes (e.g., main, urban,rural, dirt, etc.) in the area and general navigational patterns, and/ortrends in the relevant area. For example, if a large percentage ofvehicles typically remain on a main road in a particular area or region,map information for a small distance along side roads may be retrieved.If a vehicle does in fact navigate onto a side road that is typicallyless traveled, then more map information for the side road (e.g., for agreater distance along the side road) may be obtained and/or transmittedto the vehicle. In some embodiments, a tile may have a rectangle shape,a square shape, a hexagon shape, or the like, or a combination thereof.One skilled in the art would understand that the shape of a tile is notlimited to the shapes described in this disclosure. For example, a tilemay include an irregular shape (e.g., determined according to at least aboundary of a jurisdiction (states, counties, cities, or towns) or otherregions (e.g., streets, highways) and constraints). Alternatively oradditionally, a tile may include a portion of any shape disclosedherein.

FIGS. 28E-28H illustrates exemplary map tiles for potential travelenvelopes for a vehicle, consistent with disclosed embodiments. Asillustrated in FIGS. 28E-28H, server 2701 may divide area 2800 (or asmaller or a larger area) into multiple tiles 2811. Server 2701 may alsobe configured to determine one or more tiles that at least partiallyoverlap with the potential travel envelope of vehicle 2702. For example,as illustrated in FIG. 28E, server 2701 may determine an area 2831having tiles that intersect with or are within boundary 2821 of apotential travel envelope for vehicle 2702. As another example, asillustrated in FIG. 28F, server 2701 may determine an area 2832 havingtiles that intersect with or are within boundary 2822 of a potentialtravel envelope for vehicle 2702. As another example, as illustrated inFIG. 28G, server 2701 may determine an area 2833 having tiles thatintersect with or are within boundary 2823 of a potential travelenvelope for vehicle 2702. As another example, as illustrated in FIG.28H, server 2701 may determine an area 2834 having tiles that intersectwith or are within boundary 2824 of a potential travel envelope forvehicle 2702. In some embodiments, server 2701 may transmit the mapinformation and/or data relating to one or more road segments in thedetermined area to vehicle 2702.

FIGS. 29A and 29B illustrate exemplary map tiles, consistent withdisclosed embodiments. As illustrated in FIG. 29A, an area (or a map)may be divided into a plurality of tiles at different levels. Forexample, in some embodiments, an area may be divided in a plurality oftiles at level 1, each of which may be divided into a plurality of tilesat level 2. Each of the tiles at level 2 may be divided into a pluralityof tiles at level 3, and so on. FIG. 29B illustrates a plurality oftiles in a region. Alternatively or additionally, a region or a countrymay be divided into a plurality of tiles based on jurisdictions (e.g.,states, counties, cities, towns) and/or other regions (e.g., streets,highways). In some embodiments, the dimension of a tile may vary. Forexample, as illustrated in FIGS. 29A and 29B, an area (or a map) may bedivided into different levels, and a tile at a particular level may havea particular dimension.

In some embodiments, a tile may be presented in a data blob, which mayinclude a metadata block (e.g., 64 bytes), a signature block (e.g., 256bytes), and an encoded map data block (e.g., various sizes in the MapBoxformat).

In some embodiments, server 2701 may retrieve data relating to one ormore tiles in a region and transmit the data to processing unit 2706via, for example, network 2705.

Alternatively or additionally, processing unit 2706 may retrieve thedata relating to one or more tiles from a storage device. For example,processing unit 2706 may receive one or more road segments from server2701 as described elsewhere in this disclosure. Processing unit 2706 mayalso store the received one or more road segments into a local storageand load one or more tiles included in the one or more road segmentsinto a memory for processing. Alternatively, rather than receiving oneor more road segments from server 2701, processing unit 2706 maycommunicate with a local storage configured to store one or more roadsegments and retrieve the data relating to the one or more road segmentsfrom the local storage.

In some embodiments, processing unit 2706 may retrieve the data (e.g.,map information) relating to one or more tiles based on the location ofthe vehicle. For example, processing unit 2706 may determine thevehicle's current location (as described elsewhere in this disclosure)and determine a first tile in which the current location resides.Processing unit 2706 may also retrieve the first tile and one or more(or all) tiles adjacent to the first tile. Alternatively oradditionally, Processing unit 2706 may retrieve one or more (or all)tiles within a predetermined distance from the first tile. Alternativelyor additionally, processing unit 2706 may retrieve one or more (or all)tiles within a predetermined degree of separation from the first tile(e.g., one or more (or all) tiles that are within the second degree ofseparation; that is, one or more (or all) tiles that are adjacent to thefirst tile or adjacent to a title that is adjacent to the first tile).When vehicle 2702 moves to a second tile, processing unit 2706 mayretrieve the second tile. Processing unit 2706 may also retrieve thefirst tile and one or more (or all) tiles adjacent to the second tile.Alternatively or additionally, processing unit 2706 may retrieve one ormore (or all) tiles within a predetermined distance from the secondtile. Alternatively or additionally, processing unit 2706 may retrieveone or more (or all) tiles within a predetermined degree of separationfrom the second tile (e.g., one or more (or all) tiles that are withinthe second degree of separation; that is, one or more (or all) tilesthat are adjacent to the second tile or adjacent to a title that isadjacent to the second tile). In some embodiments, processing unit 2706may also delete (or overwrite) the first tile and/or the previouslyretrieved tiles that are not adjacent to the second tile. Alternativelyor additionally, processing unit 2706 may delete (or overwrite) one ormore previously retrieved tiles that are not within a predetermineddistance from the second tile. Alternatively or additionally, processingunit 2706 may delete (or overwrite) one or more (or all) previouslyretrieved tiles that are not within a predetermined degree of separationfrom the second tile.

FIG. 30 illustrates an exemplary process for retrieving one or moretiles. As illustrated in FIG. 30, processing unit 2706 (and/or server2701) may be configured to determine that the location of vehicle 2702is in tile 5 at time point 1. Processing unit 2706 may also beconfigured to retrieve (or load) adjacent tiles 1-4 and 6-9. At timepoint 2, processing unit 2706 (and/or server 2701) may be configured todetermine that the location of vehicle 2702 moves to tile 3 from tile 5.Processing unit 2706 may be configured to retrieve (or load) new tiles10-14, which are adjacent to tile 3. Processing unit 2706 may also beconfigured to keep tiles 2, 3, 5, and 6, and delete tiles 1, 4, and 7-9.As such, processing unit 2706 may retrieve (or load) a subset of thetiles (e.g., 9 tiles) at a time to reduce the memory usage and/orcomputation load. In some embodiments, processing unit 2706 may beconfigured to decode a tile before loading the data into the memory.

Alternatively or additionally, processing unit 2706 may determine asubpart of a tile that its location falls within the tile and load thetiles adjacent to the subpart. By way of example, as illustrated in FIG.31A, processing unit 2706 may determine that the location of vehicle2702 is in a sub-tile (a sub-tile having a pattern of dots in tile 5)and load map data of the tiles that are adjacent to the sub-tile (i.e.,tiles 4, 7, and 9) into the memory for processing. As another example,as illustrated in FIG. 31B, processing unit 2706 may determine that thelocation of vehicle 2702 is in the left-top sub-tile of tile 5.Processing unit 2706 may also load the tiles adjacent to the left-topsub-tile of tile 5 (i.e., tiles 1, 2, and 4). As such, processing unit2706 may retrieve (or load) a subset of the tiles (e.g., 4 tiles) at atime to reduce the memory usage and/or computation load. As anotherexample, as illustrated in FIG. 31C, processing unit 2706 may determinethat the location of vehicle 2702 is in the right-top sub-tile and loadmap data of the tiles that are adjacent to the sub-tile (i.e., tiles 2,3, and 6) into the memory for processing. As another example, asillustrated in FIG. 31D, processing unit 2706 may determine that thelocation of vehicle 2702 is in the right-bottom sub-tile and load mapdata of the tiles that are adjacent to the sub-tile (i.e., tiles 6, 8,and 9) into the memory for processing. In some embodiments, processingunit 2706 may be configured to decode a tile before loading the datainto the memory.

FIG. 32 is a flowchart showing an exemplary process for providing one ormore map segments to one or more vehicles, consistent with the disclosedembodiments. One or more steps of process 3200 may be performed by aprocessing device (e.g., processing unit 2706), a device associated withthe host vehicle (e.g., vehicle device 2703), and/or a server (e.g.,server 2701). While the descriptions of process 3200 provided below useserver 2701 as an example, one skilled in the art would appreciate thatone or more steps of process 3200 may be performed by a processingdevice (e.g., processing unit 2706) and a vehicle device (e.g., vehicledevice 2703). For example, processing unit 2706 may determine apotential travel envelope based on navigational information. Inadditional to or alternative to receiving map data from server 2701,processing unit 2706 may retrieve portion of map data relating to thepotential travel envelope from a local storage and load the retrieveddata into a memory for processing.

At step 3201, navigational information may be received from a vehicle.For example, server 2701 may receive navigational information fromprocessing unit 2706 via, for example, network 2705. In someembodiments, the navigational information received from processing unit2706 may include an indicator of a location of the vehicle, an indicatorof a speed of the vehicle, and an indicator of a direction of travel ofthe vehicle. For example, processing unit 2706 may be configured toreceive data from vehicle device 2703 or directly from one or moresensors, including, for example, a GPS device, a speed sensor, anaccelerometer, a suspension sensor, or the like, or a combinationthereof. Processing unit 2706 may also be configured to determinenavigational information, such as the vehicle's location, speed, and/ordriving direction, based on the received data. Processing unit 2706 mayalso be configured to transmit the navigational information to server2701 via, for example, network 2705. Alternatively or additionally,processing unit 2706 may be configured to transmit the sensor data toserver 2701. Server 2701 may be configured to determine navigationalinformation, which may include the location of vehicle 2702, speed ofvehicle 2702, and/or direction of travel of vehicle 2702, based on thereceived sensor data.

In some embodiments, processing unit 2706 may transmit the navigationalinformation (and/or sensor data) to server 2701 continuously.Alternatively, processing unit 2706 may transmit the navigationalinformation (and/or sensor data) to server 2701 intermittently. Forexample, processing unit 2706 may transmit the navigational information(and/or sensor data) to server 2701 a number of times over a period oftime. By way of example, processing unit 2706 may transmit thenavigational information (and/or sensor data) to server 2701 once perminute. Alternatively, processing unit 2706 may transmit thenavigational information when it has access to a more reliable and/orfaster network (e.g., having a stronger wireless signal, via a WIFIconnection, etc.).

At step 3202, the received navigational information may be analyzed anda potential travel envelope for the vehicle may be determined. Forexample, server 2701 may analyze vehicle 2702's location, speed, and/ordriving direction, and determine a potential travel envelope, which mayinclude a determined area relative to vehicle 2702. By way of example,as illustrated in FIG. 28A, server 2701 may determine an area thatencompasses the location of vehicle 2702 and determine a potentialtravel envelope having a boundary 2811, based on the determined area.

In some embodiments, server 2701 may be configured to determine apotential travel envelope extending from the location of vehicle 2702and surrounding the location of vehicle 2702. For example, asillustrated in FIG. 28A, server 2701 may determine a line 2802 passingthe location of vehicle 2702 (or the centroid of vehicle 2702). Server2701 may also be configured to determine a side of the boundary of thepotential travel envelope in the direction of travel of vehicle 2702 anddetermine another side of the boundary of the potential travel envelopein a direction opposite to the direction of travel. By way of example,server 2701 may determine the upper boundary of the potential travelenvelope in the direction of travel of vehicle 2702 and determine thelower boundary of the potential travel envelope in the directionopposite to the direction of travel of vehicle 2702. In someembodiments, the potential travel envelope may extend further along thedirection of travel of the vehicle than in a direction opposite to thedirection of travel of the vehicle. For example, as illustrated in FIG.28A, the upper boundary of the potential travel envelope may have adistance 2803 from line 2802 (or a first distance from the location ofvehicle 2702), and the lower boundary of the potential travel envelopehave a distance 2804 from line 2802 (or a second distance from thelocation of vehicle 2702). Distance 2803 may be greater than distance2804 (and/or the first distance may be greater than the seconddistance). In some embodiments, the location of a centroid of theboundary may be offset from the location of vehicle 2702 along thedirection of travel of vehicle 2702.

Alternatively or additionally, in determining the potential travelenvelope for vehicle 2702, server 2701 may take a potential traveldistance over a period of time (or a time window) into account. Forexample, server 2701 may determine a potential travel distance over apredetermined amount of time and determine the potential travel envelopeincluding the potential travel distance. In some embodiments, thepotential travel distance over the predetermined amount of time may bedetermined based on the location of vehicle 2702 and/or the speed ofvehicle 2702. In some embodiments, server 2701 may determine thepotential travel envelope further based on a selected or predeterminedtime window. The time window may be selected or determined based on theindicator of the speed of the vehicle. The predetermined amount of time(or a time window) may be in the range of 0.1 seconds to 24 hours. Insome embodiments, the predetermined amount of time (or a time window)may be restricted into subranges of 0.1 seconds to 1 second, 1 second to5 seconds, 5 to 10 seconds, 7.5 to 50 seconds 15 to 60 seconds, 1 minuteto 5 minutes, 5 to 10 minutes, 10 to 60 minutes, 1 hour to 5 hours, 5 to10 hours, and 10 to 24 hours. In some embodiments, the predeterminedamount of time (or a time window) may be determined based on thetransmission frequency of the navigational information from vehicle 2702to server 2701. For example, server 2701 may determine the predeterminedamount of time (or a time window) based on the interval between twotransmissions of the navigational information from vehicle 2702. Server2701 may determine a longer time period for determining the potentialtravel distance for a longer transmission interval.

In some embodiments, a potential travel envelope may include a boundary.The boundary of the potential travel envelope may have a shape,including, for example, a triangular shape, a quadrilateral shape, aparallelogram shape, a rectangular shape, a square (or substantiallysquare) shape, a trapezoid shape, a diamond shape, a hexagon shape, anoctagon shape, a circular (or substantially circular) shape, an ovalshape, an egg shape, an irregular shape, or the like, or a combinationthereof. FIGS. 28A-28D illustrate exemplary potential travel envelopesfor a vehicle in an area 2800, consistent with disclosed embodiments. Asillustrated in FIG. 28A, server 2701 may determine a potential travelenvelope having a boundary 2811, which may include a trapezoid shape,for vehicle 2702. As another example, as illustrated in FIG. 28B, server2701 may determine a potential travel envelope having a boundary 2812,which may include an oval shape, for vehicle 2702. As another example,as illustrated in FIG. 28, server 2701 may determine a potential travelenvelope having a boundary 2813, which may include a triangle shape, forvehicle 2702. As another example, as illustrated in FIG. 28, server 2701may determine a potential travel envelope having a boundary 2814, whichmay include a rectangular shape, for vehicle 2702.

In other embodiments, processing unit 2706 (and/or vehicle device 2703)may determine a potential travel envelope based on the navigationalinformation.

At step 3203, one or more map segments may be sent to vehicle 2702. Insome embodiments, the map segment(s) may include map information for ageographical region at least partially overlapping with the potentialtravel envelope of vehicle 2702. For example, server 2701 may send toprocessing unit 2706 the one or more map segments including map data ofa geographical region at least partially overlapping with the potentialtravel envelope of vehicle 2702 via network 2705.

In some embodiments, the one or more map segments include one or moretiles representing a region of a predetermined dimension. For example,as illustrated in FIG. 28E, server 2701 may determine one or more tiles2831 that at least partially overlaps with the potential travel envelopeof vehicle 2702 (i.e., the potential travel envelope having boundary2821) and transmit map data relating to tiles 2831 to vehicle 2702 vianetwork 2705.

In some embodiments, the dimension of a tile sent to processing unit2706 may vary. For example, as illustrated in FIGS. 29A and 29B, an area(or a map) may be divided into different levels, and a tile at aparticular level may have a particular dimension. In some embodiments,the predetermined dimension of the tile(s) sent to processing unit 2706may be in a range of 0.25 to 100 square kilometers, which may berestricted in a subrange of 0.25 square kilometers to 1 squarekilometer, 1 square kilometers to 10 square kilometers, and 10 to 25square kilometers, 25 to square kilometers, and 50 to 100 squarekilometers. In some embodiments, the predetermined dimension of thetile(s) may be less than or equal to ten square kilometers.Alternatively, the predetermined dimension of the tile(s) may be lessthan or equal to one square kilometer. Alternatively, the predetermineddimension of the tile(s) may be less than or equal to ten squarekilometers. In some embodiments, a tile may have a rectangle shape, asquare shape, a hexagon shape, or the like, or a combination thereof.

In some embodiments, the map information sent to processing unit 2706may include a polynomial representation of a target trajectory along theone or more road segments, as described elsewhere in this disclosure.For example, the map information may include a polynomial representationof a portion of a road segment consistent with the disclosed embodimentsillustrated in FIG. 9A, FIG. 9B, and FIG. 11A. For example, the mapinformation may include a polynomial representation of a targettrajectory that is determined based on two or more reconstructedtrajectories of prior traversals of vehicles along the one or more roadsegments.

In some embodiments, after receiving the one or more road segments,processing unit 2706 and/or vehicle device 2703 may navigate vehicle2702 according to the one or more road segments, as described elsewherein this disclosure. For example, vehicle 2702 may be configured toperform one or more navigational actions (e.g., making a turn, stoppingat a location, etc.) based on the received one or more road segments.Alternatively or additionally, vehicle 2702 may be configured to performone or more navigational actions based on the polynomial representationof a target trajectory along the one or more road segments.

In some embodiments, processing unit 2706 may receive the one or moreroad segments and store the one or more road segments into a storagedevice. Processing unit 2706 may also load one or more tiles included inthe one or more road segments into a memory for processing. For example,as illustrated in FIG. 30, processing unit 2706 (and/or server 2701) maybe configured to determine that the location of vehicle 2702 is in tile5 at time point 1. Processing unit 2706 may also be configured toretrieve (or load) adjacent tiles 1-4 and 6-9. At time point 2,processing unit 2706 (and/or server 2701) may be configured to determinethat the location of vehicle 2702 moves to tile 3 from tile 5.Processing unit 2706 may be configured to retrieve (or load) new tiles10-14, which are adjacent to tile 3. Vehicle 2702 may also be configuredto keep tiles 2, 3, 5, and 6, and delete tiles 1, 4, and 7-9.

Alternatively or additionally, processing unit 2706 may determine asubpart of a tile that its location falls within a tile and load thetiles adjacent to the subpart. By way of example, as illustrated in FIG.31A, processing unit 2706 may determine that the location of vehicle2702 is in a sub-tile (a sub-tile having a pattern of dots in tile 5)and load map data of the tiles that are adjacent to the sub-tile (i.e.,tiles 4, 7, and 9) into the memory for processing. As another example,as illustrated in FIG. 31B, processing unit 2706 may determine that thelocation of vehicle 2702 is in the left-top sub-tile of tile 5.Processing unit 2706 may also load the tiles adjacent to the left-topsub-tile of tile 5 (i.e., tiles 1, 2, and 4). In some embodiments,processing unit 2706 may be configured to decode the tiles beforeloading the data into the memory.

In some embodiments, rather than receiving the one or more road segmentsfrom server 2701 through network 2705, processing unit 2706 may retrievethe one or more road segments from a local storage. For example,processing unit 2706 may determine the potential travel envelope basedon the analysis of the navigational information and determine one ormore road segments including map information for a geographical regionat least partially overlapping with the potential travel envelope ofvehicle 2702. Processing unit 2706 may also retrieve the data of the oneor more road segments from a local storage. In some embodiments,processing unit 2706 may load the data of the one or more road segmentsinto its memory for processing.

Electronic Horizon: Lane Information

Consistent with the present disclosure, the disclosed system providesvehicles with relevant information tailored to each particular vehicle.For example, server 2701 may determine one or more tiles 2831 that atleast partially overlaps with the potential travel envelope of vehicle2702, but instead of (or in addition to) transmitting map data relatingto tiles 2831 to processing unit 2706, server 2701 may transmit relevantlane information. Alternatively, as discussed above, server 2701 mayretrieve data relating to one or more tiles in a region and transmit themap data to processing unit 2706 via, for example, network 2705.Thereafter, processing unit 2706 may determine from the received mapdata relevant lane information tailored to each particular vehicle, andcovey the relevant lane information to vehicle device 2703. The relevantlane information may include details regarding objects that vehicle 2702may encounter along its driving path (e.g., spline, road geometries,road structures and features, etc.). In one embodiment, the relevantinformation may be determined based on the location and/or the directionof travel of a particular vehicle. For example, processing unit 2706 (orserver 2701) may determine which lane vehicle 2702 is currently in fromamong the available lanes (i.e., the ego lane) and pass along to vehicledevice 2703 information associated with that lane. Processing unit 2706(or server 2701) may also determine and convey to vehicle device 2703the position along a spline representative of a target travel path inthe lane. The relevant information may be delivered in a customizedmanner. For example, different vehicle manufactures may request thatdifferent data be delivered to their vehicles. Additionally, therelevant information delivered to vehicle device 2703 may be dividedinto subsegments for easier deletion and management. In anotherembodiment, processing unit 2706 (or server 2701) may use a dynamic datafield (e.g., a dynamic header) when transmitting data to vehicle device2703. The dynamic data field may include relevant map information and/orrelevant lane information to vehicle 2702. Specifically, the relevantinformation provided to vehicle device 2703 may be varied dynamicallyduring a driving session based on encountered scenarios. To address thechanges in the relevant information, processing unit 2706 (or server2701) may use a dynamic data field in the data packets sent to vehicledevice 2703. The dynamic data field may allow processing unit 2706 (orserver 2701) to vary the information included in the transmitted datapackets in a manner that enables vehicle device 2703 to know what isbeing sent, where it is located in the packet, how long, etc.

Returning now to FIG. 32, at step 3204, lane information may be providedto vehicle device 2703. In some embodiments, the lane information may bespecific to one or more roads included within the potential travelenvelope of vehicle 2702. For example, processing unit 2706 (or server2701) may provide lane information for roads included within thepotential travel envelope of vehicle 2702. Consistent with someembodiments, rather than having vehicle device 2703 calculating anddetermining the lane information based on data stored in a local memory,vehicle device 2703 may receive the relevant lane information fromprocessing unit 2706 (or server 2701). For example, server 2701 maydetermine the potential travel envelope based on the analysis of thenavigational information and processing unit 2706 may determine laneinformation that may include lane assignment and/or description of thelanes in any part of the roads included within a geographical region atleast partially overlapping with the potential travel envelope ofvehicle 2702. Receiving the relevant lane information from processingunit 2706 (or server 2701) may save vehicle device 2703 from receivingand storing a large amount of data for one or more map segments and fromprocessing the data to determine the relevant lane information.Moreover, the lane information determined by processing unit 2706 may bemore accurate than any lane information determined locally by vehicledevice 2703.

In a first aspect of the disclosure, the lane information may includelane assignment information relevant to vehicle 2702. Consistent withthe present disclosure, the lane assignment information may include adescription of the lanes in a determined distance ahead of vehicle 2702.The determined distance may be in the range of 1 meter and 5 kilometers.In some embodiments, the distance may be restricted into any subranges,for example, 1 to 50 meters, 5 to 500 meters, 10 to 200 meters, 10 to750 meters, 5 meters to 1 kilometer, 50 meters to 2 kilometers, 100meters to 2.5 kilometers, and more. The distance may also be derivedfrom a time parameter and a speed parameter as described above withreference to section relating to “Map Management Using an ElectronicHorizon.” Specifically, the lane information may include a list of lanesin the part of road that vehicle 2702 currently rides and an indicationas to which lane is the ego one. Each lane may include a list of rightneighbor lanes, a list of left neighbor lanes, and a list of successors.Each lane may also include a Boolean indication as to whether it is alane in the direction of a driving direction of vehicle 2702 or anoncoming lane. For example, the lane assignment information may includedetails regarding an ego lane associated with vehicle 2702 and one ortwo neighboring lanes to the right and/or to the left of the ego lane.In one embodiment, the output of the system may be configurable suchthat the number of lanes for which assignment information is providedmay vary. In some embodiments, a more detailed designation (two bits, abyte, etc.) of lanes may be used to accommodate designation of lanes inmultiple possible directions (e.g., lanes diverging from anintersection, multiple possible U-turns, etc.).

In a second aspect of the disclosure, the lane information may include adescription of the lanes in any part of the roads included within thepotential travel envelope of vehicle 2702. Consistent with the presentdisclosure, the description of the lanes may include at least one of: alocation of vehicle 2702 in the ego lane, a driving path profile (e.g.,a suggested driving path profile (vertical, longitudinal, or both) inthe ego lane), stop points (e.g., points on the ego lane where there isa stop line or a virtual stop line that is marked on the map but notphysically apparent. In some cases, virtual stop lines may be createdbased on crowd sourcing information regarding the typical stoppingposition of vehicles at a particular location), lane marks points (e.g.,points on the ego lane where the lane marks changes, for example, fromdashed line to solid line), lane topology (e.g., the relations betweenlanes), and more. In one embodiment, the description of the lanes mayinclude a description of the lanes ahead of vehicle 2702. For example,the description of the lanes may include a list of lanes and thetopology relations (i.e., the connectivity) between them. Specifically,each lane may have successors, predecessors and neighbors. In case oflane split, a lane has more than one successor, and in case of lanesmerge a lane has more than one predecessor. Provisioning of the laneinformation may enable vehicle device 2703 to navigate vehicle 2702autonomously, or to supervise decisions made by a human driver, andpotentially intervene in case of bad judgement and/or dangerousdecisions by the driver (e.g., decisions that may violate a safetypolicy or a driving policy such as RSS developed by Mobileye VisionTechnology Ltd of Jerusalem, Israel). For example, the vehicle'snavigation system associated with vehicle device 2703 may use thedescription of the lanes (and the lane assignment information) tocontrol the driving path of the vehicle 2702, in initiating a lanechange maneuver, in staying within lane boundaries, and more. In anotherembodiment, the description of the lanes may also assist vehicle device2703 in navigating relative to detected target vehicles. For example,vehicle device 2703 may be able to determine or predict a path one ormore target vehicle will travel based on mapped and sampled drivablepaths for adjacent or intersecting lanes. The lane information may alsobe useful when vehicle device 2703 is collecting information relating toa road segmenting (e.g., automatically determining and reporting on anaverage speed traveled within a certain lane by vehicle 2702, targetvehicles, etc.).

In some embodiments, the lane information may include lane assignmentinformation and a description of the lanes in roads that vehicle 2702may access during a determined time window. For example, the time widowmay be selected or determined based on the indicator of the speed of thevehicle. The determined time window may be in the range of 0.1 secondsto 24 hours. In some embodiments, the time window may be restricted intosubranges of 0.1 seconds to 1 second, 1 second to 5 seconds, 5 to 10seconds, 7.5 to 50 seconds, 15 to 60 seconds, 1 minute to 5 minutes, 5to 10 minutes, 10 to 60 minutes, 1 hour to 5 hours, 5 to 10 hours, and10 to 24 hours. As mentioned above, the time window may be determinedbased on the transmission frequency of the navigational information fromvehicle 2702 and server 2701.

The communication between server 2701 and vehicle device 2703 and/or thecommunication between processing unit 2706 and vehicle device 2703 maybe governed by a dedicated communication protocol. In some cases, thecommunication protocol may be a single-direction protocol such that datamay be sent from processing unit 2706 (or server 2701) to vehicle device2703. A message in the single-direction communication protocol mayinclude at least two parts: a dynamic data field (e.g., a dynamicheader) for describing the objects included in the message and payloaddata may include different types of objects associated with the laneinformation. The payload data may be arranged in the same order as itappears in the dynamic header (e.g., the type of objects and theirlength fields). The single-direction protocol may present an efficienttradeoff. By using pre-configured parameters (e.g., how many seconds orwhat distance worth of data needs to be included in the potential travelenvelope) communication may be streamlined and/or simplified.Potentially, the use of the single direction protocol may also providesecurity advantages since processing unit 2706 (or server 2701) is notexposed to corrupt information. In other cases, the communicationprotocol may be a two-direction protocol such that data may be sent fromprocessing unit 2706 (or server 2701) to vehicle device 2703 and datamay also be sent from vehicle device 2703 to processing unit 2706 (orserver 2701). A response message in the two-direction communicationprotocol from vehicle device 2703 to processing unit 2706 (or server2701) may include details on the accuracy of the information sent by theprocessing unit 2706 (or server 2701). For example, the response messagemay be used to confirm the location and/or identify of certain objectsin the environment of vehicle 2702.

FIG. 33 is a schematic diagram that depicts a structure of messages thatmay be sent to vehicle device 2703 from processing unit 2706 (or server2701). Consistent with the present disclosure, message 3300 includes adynamic header that describes structure of the message, the size of eachobject in the message and content of each object within the messagepayload. Header 3310 may be used as a dictionary for parsing the messagepayload and reconstructing the lane information. In one embodiment,header 3310 may also provide information on how to decode the dataprovided in payload 3320. Header 3310 may include a version field 3311(i.e., the number of version used); a number of objects field 3312(i.e., the number of objects contained in the message); a size field3313 (i.e., the size of payload objects in the current message), asegment ID field 3314 (i.e., the IDs of map segments included in thismessage); and a type and length array of the objects field 3315 (i.e., alist of object descriptors that specify for each of the objects in themessage its type and size. The length of this list may be the number ofobjects in the payload. The dynamic nature of header 3310 may enableprocessing unit 2706 (or server 2701) to transmit messages containinginformation likely to be used by vehicle device 2703 and avoid sendingirrelevant information to vehicle device 2703. Thus, for example, thedynamic nature of header 3310 may reduce the size of messages exchangedthrough network 2705.

In some embodiments, payload 3302 may include a SegmentsInEH object3321, a DrivablePathSpline object 3322, a DrivablePathProfile object3323, and one or more payload objects 3324. SegmentsInEH object 3321 maybe sent by processing unit 2706 (or server 2701) every time there is achange in the list of included map segments within the potential travelenvelope of vehicle 2702 (new map segment added, or old map segmentremoved). In one example, SegmentsInEH object 3321 may be a vector ofsegmentIDs (e.g., 32-bit unsigned integer each) listing the map segmentIDs that are covered in the potential travel envelope of vehicle 2702.Vehicle device 2703 may delete from its local memory data associatedwith SegmentsInEH that is not included in the list. DrivablePathSplineobject 3322 may include data of a spline associated with the drivablepath. In one example, DrivablePathSpline object 3322 may include thespline ID and lane information that describes relationship between thedrivable path spline and the lanes in any part of the roads includedwithin the potential travel envelope of vehicle 2702.DrivablePathProfile object 3323 may include an array of values along thedrivable path spline. For example, the data within the drivable pathprofile may include the average speed on different points (e.g.,longitudinal position) on the driving path.

In some embodiments, the lane information transmitted to vehicle device2703 from processing unit 2706 (or server 2701) may be included inpayload objects 3324. Examples for payload objects 3324 may include atleast one of: a lane topology object that describes the relationsbetween lanes in the driving path; a lane mask object that representsthe lanes to which a spline relates; driving path borders objects thatdescribe congruent of a lane mark spline or road edge spline to thedriving path; merge/split points that includes informing of a split ormerge point in the map coordinate system; a road edge changed pointsobject that contains an array of velocity points on the spline at whichroad edge type change occurs; a sign landmark object that describestraffic signs and traffic lights; a pole landmark object that includesdetails on poles located adjacent the ego lane; and more. In otherwords, payload objects 3324 transmitted to vehicle device 2703 maydescribe the road geometrics, the semantics (e.g., the lanes topology inany part of the roads included within the potential travel envelope ofvehicle 2702), and an indication where the host vehicle is locatedrelative to the lanes' topology. For example, when driving in a highway,vehicle device 2703 may receive through message 3300 an indication thatthe right lane will become a lane for exiting the highway in 350 meters.FIG. 34 depicts an example of an actual message sent to a host vehicle.

In additional embodiments, after receiving the one or more map segmentsand the lane information, vehicle device 2703 may navigate vehicle 2702as described elsewhere in this disclosure. For example, vehicle device2703 may be configured to perform one or more navigational actions(e.g., making a turn, stopping at a location, accelerating, braking,etc.) based on the received one or more map segments and the laneinformation. Alternatively or additionally, vehicle device 2703 may beconfigured to perform one or more navigational actions based on thepolynomial representation of a target trajectory. In yet otherembodiments, after receiving the one or more map segments and the laneinformation, vehicle device 2703 may use computer vision and AI tocreate an environmental model at any given point or location. In yetother embodiments, after receiving the one or more map segments and thelane information, vehicle device 2703 may create a map (e.g., such as asparse map) discussed in further detail earlier in this disclosure. Inyet other embodiments, after receiving the environmental model, the oneor more map segments and the lane information, vehicle device 2703 mayapply driving policies to supervise a human driver and intervene (e.g.,cause the vehicle to perform one or more navigational actions, such asturning, stopping at a location, accelerating, braking, etc.) if one ormore actions by the human driver would lead to a violation of at leastone driving policy rule and/or a safety constraint associated with adriving policy.

Additional Electronic Horizon Features

As described elsewhere in this disclosure, the disclosed systems andmethods may use an electronic horizon to efficiently load or use mapdata in a pre-defined area (e.g., in a region having a predeterminedradius). The disclosed systems and methods may optimize bandwidthconsumption and provide map users with the most relevant data whileconsidering vehicle position, ego speed and trajectory. This disclosuredescribes the Electronic Horizon (“EH”) logic and building blocks asthey are extrapolated from the map and transferred via the relevantprotocols, which may be used by the disclosed systems and methods toachieve one or more functions disclosed herein.

In some embodiments, a system may include one or more EH processors(which may also be referred herein as an EH constructor) programmed todetermine map data for an area ahead of (or surrounding) a host vehicleand output the determined map data to one or more navigation systemprocessors. For example, one or more EH processors may determine mapdata for a predetermined radius (which may also be referred herein as anEH radius) ahead of the vehicle. The EH radius may cover a period oftime (e.g., a fraction of a second or a few seconds) ahead of the hostvehicle over which the host vehicle may travel at the current speed.Alternatively or additionally, the EH radius may have a minimum distance(e.g., 100 meters). The period of time and/or the minimum distance ofthe EH radius may be configured. By way of example, if the period oftime is set as 15 seconds and the host vehicle is travelling at 60 kmper hour, the one or more EH processors may obtain map data of all roadsegments (or a portion thereof) that the host vehicle may reach withinan EH radius of 250 meters.

FIG. 35 is a schematic illustration of exemplary electronic horizon mapdata, consistent with disclosed embodiments. As illustrated in FIG. 35,vehicle 3501 may travel along a road segment. One or more EH processorsmay determine an area 3502 covered by an EH radius and obtain map dataof all the road segments within area 3502. The one or more electronichorizon processors may output the map data to one or more navigationsystem processors.

The one or more navigation system processors may control the navigationsystem of the host vehicle according to the map data received from theone or more EH processors. For example, the one or more navigationsystem processors may be programmed to determine the location of thehost vehicle relative to the map associated with the map data based onoutput provided by one or more onboard sensors. The one or morenavigation system processors may also be programmed to cause the hostvehicle (e.g., via the navigation system thereof) to execute at leastone navigational maneuver according to the location of the host vehiclerelative to the map. In some embodiments, the output map data may berepresented in the map coordinates system (e.g., being represented withlatitude/longitude points).

This disclosure provides exemplary features, functionalities, andprotocols for managing electronic horizon data as described below. Theseexemplary features, functionalities, and protocols may be used in thedisclosed systems and methods, but should not be construed as limitingexamples.

Acronyms and Terminology

For purposes of brevity, the following terms and abbreviations listed inTable 1 are used in this disclosure.

TABLE 1 Acronyms and Terminology Term Description ACC Adaptive/ActiveCruise Control - enhancement of traditional cruise control that uses onboard sensing to detect vehicles ahead to maintain a set distancebetween the host vehicle and preceding vehicle CORA Center of real axleCP Control Point DBC Design by Contract - file describing structure ofmessaging in controller area network (CAN) Protocol (possibly UniversalAsynchronous Receiver Transmitter (UART) too) DP Drivable Path HD HighDynamic EH Electronic Horizon LM Lane mark ME Mobileye NED North, East,Down OEM Original Equipment Manufacturer PP Predicted Path RB RoadbookRE Road edge REM Road Experience Management RP Road Profile RTRotation - translation SPI Serial Peripheral Interface - serialcommunication channel used in one or more RH processors, mainly tocommunicate between one or more RH processors and one or more navigationsystem processors

Data Type Definitions

The following data types listed in Table 2 may be used by the disclosedsystems and methods.

TABLE 2 Exemplary Data Types Data Type Definition uint8 8-bit unsignedinteger uint16 16-bit unsigned integer Int 32-bit integer Float 32-bitfloat

Electronic Horizon Functionalities

The EH protocol may serve as the interface through which the map data issent to the outer functions and/or re-constructor (e.g., one or morenavigation system processors). In some embodiments, an EH signal mayhave a maximal output frequency. In other words, map data may be outputat a maximal frequency. For example, map data may be output at a maximalfrequency of 36 Hz (i.e., 36 times per second). In some embodiments,each map element (e.g., a map tile as described elsewhere in thisdisclosure) may be sent only once. The re-constructor (e.g., one or morenavigation system processors) may be responsible for buffering andmanaging EH map data. The EH protocol may enable an EH processor tosignal the re-constructor when map data is no longer covered by the EHradius, and the map data may be deleted from the buffer that stores theEH map data output by the one or more electronic horizon processors.

Map Data Segmentation

In some embodiments, a map (e.g., a sparse map) may be constructed ofedges. An edge may be a road between two junctions, roundabouts,roads-merges, or roads-splits. Every RB element (drivable path, lanemark, road edge, traffic signs, poles, etc.) may be contained within anedge. An Edge Segment may be a logical unit containing map data sent bythe constructor. The EH constructor may divide every edge into severalsegments. The EH constructor may signal the re-constructor to deletedata at a granularity of edge segments. Dividing edges into sub-segmentsmay reduce memory consumption at both sides (the constructor andre-constructor) since edges can be quite long.

In some embodiments, an edge segment may be identified by threeparameters:

EdgeID: ID of the original edge that the segment belongs to.

tStart: start point in meters of this segment in the original edge.

tEnd: end in meters of this segment in the original edge.

The constructor may allocate an identifier (ID) to each edge segment,which is referred herein in as segmentId. The constructor may send alist of segmentIDs to the re-constructor to indicate which edge segmentsare included in the EH radius. Every time the list of edge segmentsincluded in the EH radius changes, the constructor may send an updatedlist of segmentIDs in a separate “control” slot.

In some embodiments, the constructor may signals the re-constructor thatan edge segment can be deleted by sending an updated segmentIDs listwithout this segment ID. The re-constructor can delete any segmentIDthat is not included in the list and was already received. There-constructor may be responsible to delete any passed edge segment datathat needs to be deleted on its side. In some embodiments, segmentswhich are already presented by the re-constructer may not deletedbecause of a speed change during a drive.

In some embodiments, edges may be divided into segments having apredetermined number of (or X) meter(s), or at a point where the numberof lanes is being changed, whichever yields the minimum segment sizerequired. If a segment is smaller than X/4, a new segment may not becreated, and this data may be included in the previous segment.

In some embodiments, each edge segment may contain all the RB elementsincluded in the physical part of the edge it represents.

In some embodiments, splines (e.g., drivable paths, lane marks, roadedges, etc.) may also be split in the edge segmentation process. Aspline may be constructed of control points (CPs) and knots arrays. TheCPs and the knots may describe a b-spline mathematic model of thespline. The segmentation process may cut a spline. The cut segment mayinclude four CPs and four knots from the previous segment at thebeginning of the spline arrays, and four knots (and no CPs) from thenext segments at the end of the knots array. This may enable there-constructor to evaluate the data of an edge segment independently.

Map (Roadbook) Elements

Each transferred segment may include map objects packed in a pre-definedformat. For example, a segment may include at least one of: a spline(laneMarks, RoadEdges and DrivablePaths), a pole, a traffic sign, thenumber of lanes in segment, a relation between splines and lanes (whichspline belongs to which lane), a drivable path profile, an averagespeed/legal speed in points on the DP spline, a lane mark change points(type change), a lane mark color (paired information about the changedlane mark), a road edge type change points, a lane topology, a laneborder, or the like, or a combination thereof.

Protocol Description

As described elsewhere in this disclosure, a communication protocolmessage (e.g., an EH message) may include two parts: a header and apayload. Two types of messages, payload messages and control messages,may be used. Table 5 below illustrates an exemplary EH message.

TABLE 3 Exemplary EH Message Parameter Description Version (uint8) EHheader - describes which # objects (uint8) objects may be included inSize (uint) the EH message payload SegmentID (int) and the size of eachof them. Type + length array + size in bytes [# of objects] (4 bytes)Payload (EH object) EH payload - may include RB objects, Payload (EHobject) segment description object. Payload (EH object)

In some embodiments, the payload data may be arranged in the same orderas it appears in the header (e.g., type+length fields). The protocolbetween the constructor and re-constructor may include two types ofmessages: EH payload control and EH payload data. The payload controlmessage may include two types of objects transmitted separately:Segments in EH and Reference change point. Payload data messages may beslots that store all other types of events, as described in thefollowing descriptions of the different EH message parts and examples.Segments may include the “new segment” description (see Table 7 below)unless they contain data from previously reported new segments. In someembodiments, only the first EH packet of a segment may be included inthe “Segment Description” object.

The EH header may describe the content within the EH message payload. Itmay be used as a dictionary for parsing the message payload andre-constructing the EH data. As described elsewhere in this disclosure,an EH message may include data of one edge segment or a portion thereof.Inclusion of a partial edge segment in an EH message may happen due toconstraints/limitations of the transport layer used to carry the EH data(e.g., CAN, Serial Peripheral Interface (SPI), ETHERNET, etc.). Table 6illustrates an exemplary EH header.

TABLE 4 Exemplary EH Header Version Description # objects (uint8) Numberof EH objects that the EH message may contain Size (uint) Size ofpayload in current slot. SegmentID (int) ID of edge segment included inthis EH message. Type + length array + size in List of objectdescriptors (e.g., 4 bytes each). The bytes [# of objects] (4 bytes)object descriptors may specify type, length, and total . . . bytes foreach of the objects in the message. The length of this list may be thevalue of the #objects field. For fixed size objects, the length arraymay represent the number of objects in the slot.

In some embodiments, EH objects may be the elements comprised in the EHmessage payload. These objects help the constructor and re-constructorside describe the edge segments contained in the EH at any point. Anobject may be RB elements.

The constructor may send SegmentInEH object every time the list ofincluded edge segments in the EH radius changes with a new segmentadded, or an old segment removed. The SegmentInEH object may include avector of segmentID (uint32 each) listing the edge segment IDs that maybe covered in the EH radius. It may be sent in a separate slot (whichthe reconstructor may use for data update and not for data storage). There-constructor may delete any received segmentID that may be notincluded in the list.

A segment descriptor may describe an edge segment. This description maybe sent by the constructor in every first EH message of an edge segment.Table 7 illustrates exemplary new segment descriptors.

TABLE 7 New Segment Descriptor Data Type Description EdgeID (unit32) IDof original edge SegmentID (unit32) ID allocated by the constructor forthis segment Tstart (float) Segment start point in the edge (in meters)Tend (float) Segment end point in the edge (in meters) #ofLanes (uint8)Number of lanes in this segment Reserved (uint8) For alignment Reserved(uint8) For alignment Reserved (uint8) For alignment

A spline container may contain data of a single spline (e.g., a roadedge, a lane mark, a drivable path, etc.). Table 8 illustrates anexemplary spline object.

TABLE 5 Spline object Data Type Description SplineID (int) Original RBID of this spline U to meter (float) 1 m length in u [controlPoints] X(float) Control points array Y (float) Z (float) knots [length = num v(float) Knots array rolPoints + 4]

The Drivable Path (DP) profile object may be an array of values alongthe DP spline. The data within a profile may be the average speed onthat point (longitudinal position) on the DP. Each “v” point in thearray may describe a longitudinal point on the spline. Table 9illustrates an exemplary Drivable Path Profile object.

TABLE 6 Drivable Path Profile (DrivablePathProfile) Data TypeDescription SplineID (int) Original RB ID of the related DP spline[v_spline_coordinate] Array of points on spline in “v” units (floatarray) [Ave80Speed] The 80 percentile of the avg speed at that point(float array)

The Lane Mark Changed Points object may contain an array of “v” pointson the spline where a change in lane marking may be presented. For eachvalue in the v_spline_coordinate array, a tuple of (type, color) may bepresented in the change-events array. Table 10 illustrates an exemplaryLane Mark Changed Points object.

TABLE 7 Lane Mark Change Points Object (LaneMarkChangePoint) Data TypeDescription SplineID (int) Original RB ID of the related lane markspline [v_spline_coordinate] Array of positions on spline in “v” units(float array) (same units as spline knots) [change_events] Array of lanemark events, for example, changed (array of enums * 2) from dash tosolid

Exemplary event types are listed in Table 11 below.

TABLE 8 Event Types Event type Enumerators Lane mark typeLANE_MARK_UNKNOWN = 0 LANE_MARK_DASH LANE_MARK_SOLIDLANE_MARK_DOUBLE_SOLID LANE_MARK_DOUBLE_DASH LANE_MARK_DASH_SOLIDLANE_MARK_SOLID_DASH LANE_MARK_DECELERATION LANE_MARK_NON_SEMANTICLANE_MARK_VIRTUAL LANE_MARK_GENERIC_DOUBLE Color UNKNOWN = 0 WHITEYELLOW BLUE

The Road Edge Change Points object may contain an array of “v” points onthe spline at which RE type change occurs. An exemplary Road EdgeChanged Points object is illustrated in Table 12 below.

TABLE 9 Road Edge Change Points Objects (RoadEdgeChangePoint) Data TypeDescription SplineID (int) Original RB ID of the related lane markspline [v_spline_coordinate] Array of positions on spline in “v” units(float array) (same units as spline knots) [change_events] Array of roadedge events, for example, changed (array of enums) from guardrail toconcrete etc.

Exemplary supported types are illustrated in Table 13 below.

TABLE 10 Road Edge Change Points Objects - Supported Types Event typeEnumerators Road Edge ROAD_EDGE_UNKNOWN = 0 type ROAD_EDGE_GUARDRAIL,ROAD_EDGE_CONCRETE, ROAD_EDGE_CURB, ROAD_EDGE_NO_STRUCTURE,ROAD_EDGE_PARKING_CAR, ROAD_EDGE_PARKING_ENTRANCE

The Sign Landmark object may describe traffic signs and traffic lights.An EH message may contain an array of such objects. The system type andtraffic sign may refer to the type of sign as mapped in the Traffic SignRecognition (TSR) sensing technology ENUMs list. Table 14 illustrates anexemplary Sign Landmark object.

TABLE 11 Sign Landmark Object [SignLandmark] Data Type DescriptionObjectID (int) Original RB ID of sign systemType (uint8) Type of trafficsign Reserved (uint8) trafficSignType (uint16) Sub-type of traffic signLocation X (float) Location point in map NED coordinates Y (float) Z(float) Size W (float) Width in meters H (float) Width in meters

The pole landmark object may represents one or more poles. An EH messagemay contain an array of such objects. Table 15 illustrates an exemplarypole landmark object.

TABLE 12 Pole Landmark Object [PolesLandmark] Data Type DescriptionObjectID (int) Original RB ID of pole systemType (uint8) Reserved(uint8) Padding Byte (alignment trafficSignType (uint16) location1 X(float) Location of pole bottom Y (float) Z (float) location2 X (float)Location of pole upper edge Y (float) Z (float)

Lane topology may describe the relations between lanes (DPs). Each lanemay have successors, predecessors, and neighbors. Lanes and DP may beequivalent in the roadbook. In some embodiments, each lane may have oneDP and each DP may belong to one lane. In some embodiments, if a lanesplit occurs, a lane may have more than one successor, and when lanesmerge, a lane may have more than one predecessor. In some embodiments,the array size for successor/predecessor IDs may be currently 3.

Table 16 illustrates a DpSuccessor object. Table 17 illustrates aDpPredecessor object.

TABLE 13 DpSuccessor Object Data Type Description DpId (int) This DP IDSuccessor_Id [int] Other DP ID

TABLE 14 DpPredecessor Object Data Type Description DpId (int) This DPID Predecessor_Id [int] Other DP ID

Lane neighbors may be the lanes to the right and left of a lane. A laneneighbor may be defined from u=x to u=y (“u” of this DP). A lane mayhave more than one neighbor at one of its sides (in different “u”intervals). Table 18 illustrates an exemplary DpNeighbors object.

TABLE 15 DpNeighbors Object Data Type Description DpId (int) This DP IDotherDPid (int) The DP neighbor ID From (float32) U value on DP To(float32) U value on DP Direction (int8) DP position referenced to ego(left/right) Oncoming (bool) Equals 1 if the DP in the opposite drivingdirection.

Direction may use a set of three enum values to position a DP inreference to the ego DP: “From” and “to” values describe the congruentpart of the spline where the lane neighbors object may be relevant;Invalid=−1, right=0, left=1; The “U” value may be used as a descriptorfor the longitudinal position on the spline when U=0 may be located atthe beginning of the DP spline.

The DpBorders object may describe the congruence of a lane mark spline,or road edge spline, to the DP. This object may define the interval ofthe DP and the border spline (road edge or lane mark) where the twosplines may be tangent. Table 19 illustrates an exemplary DpBordersobject.

TABLE 16 LaneBorder Object [DpBorders] Data Type Description dPId (int)This DP ID borderId Border Spline ID dpStartU (float) U on DP whenborder relation starts dpEndU (float) U on DP when border relation endsBorderSratU (float) U on border spline when border relation startsBorderEndU (float) U on border spline when border relation endsDirection (int8) Right = 0, Left = 1 borderSplineType(uint8) LaneMark =0, RoadEdge = 1

In some embodiments, all “U” values may correspond to spline pointswhere borders may be shared. FIG. 36 is a schematic diagram of exemplarylane borders, consistent with disclosed embodiments.

In some embodiments, an object of a lane intersection point type maydefine a split/merge point by its X-Y-Z coordinates in the RB coordinatesystem. “Dps out” and “Dps in” may be always expressed in terms of amaximum size of two items where a “−1” value in the dpId field signalsthe end of array (when there may be fewer than three items). Table 20illustrates an exemplary LaneIntersectionPoints object.

TABLE 17 LaneIntersectionPoints Object Description Object DescriptiondpsIn [2] DP id(int) Arrays always in max length of 2 U (float) dpsOut[2] DP id(int) Arrays always in max length of 2 U (float) X (float) Y(float) Z (float) numDpsIn (uint8) Actual num Dps in numDpsOut (uint8)Actual num Dps in

Table 21 illustrates an exemplary Reference Point Change Event object.

TABLE 18 Reference Point Change Event Object (ReferencePointChangeEvent)Data Type Description PLT_time (uint32) T tX (double) tY (double) tZ(double) R Roll (double) Pitch (double) Yaw (double) GPS ReferenceLongitude (double) Latitude (double) Altitude (double)

Map Coordinate System Origin Point Change Event

To avoid precision errors producing an accumulated bias over distances,the one or more EH processors may dynamically change the map coordinatesystem's origin point. This may happen when the vehicle's distance fromthe origin point becomes larger than a predetermined threshold. All themap objects may be transformed at this point according to the newcoordinates system. When this event occurs, the one or more EHprocessors may send the ReferencePointChangeEvent object to signal thatmap coordinates have been changed. EH previous segments may be flashedon the re-constructor side and transmitted by the constructor in the newcoordinate system. When the change occurs, the localization outputchanges to a “not localized” state. FIG. 37 illustrates an exemplaryprocess for changing the EH coordinates system, consistent withdisclosed embodiments.

Table 19 below describes the type values and the meaning of the lengthvalue for each of the EH objects that may be used in the disclosedsystems and methods. The objects having a length of control pointsvector may define the form of a spline (e.g., control points amount) inthe object definition in a header.

TABLE 19 Object Descriptors Type (enum/uint8) Meaning of Length (uint8)NEW_SEGMENT_DESCRIPTION = 2 Always max 1 SEGMENT_IN_EH Length ofSegmentID vector SPLINE_LANE_MARK Length of controlPoints vectorSPLINE_ROAD_EDGE Length of controlPoints vector SPLINE_DRIVABLE_PATHLength of controlPoints vector LANE_MARK_CHANGE_POINT Length ofv_spline_coordinate vector DRIVALBE_PATH_PROFILE Length ofv_spline_coordinate vector SIGN_LANE_MARK Number of SignLandmarksPOLES_LANE_MARK Number of PolesLandmarks REFERENCE_POINT_CHANGE_EVENTAlways max 1 ROAD_EDGE_CHANGE_POINT Length of v_spline_coordinate vectorDPSUCCESSOR Num of successors DPPREDECESSOR Num of predecessorsDPNEIGHBORS Num of neighbors DPBORDERS Num of bordersLANEINTERSECTOPNPOINTS Number of lane intersection points

By way of example, three exemplary EH Messages are provided in Table 23,Table 24, and Table 25 below for illustration purposes, which should notbe construed as limiting examples.

TABLE 20 Exemplary Message with Segment Descriptor (First Message inEdge Segment) Version = 1.0 # of objects = 5 Size = 640 B SegmentID ==SegmentID of new Segment (included in the segmentInEH vector that formspart of this message) New Segment Description 1 DrivablePathSpline 32DrivablePathProfile 25 SignLandmark 3 New Segment DescriptionDrivablePathSpline[32 control points and 36 knots]DrivablePathProfile[25 v_spline_coordinate and 25 Ave80Speed]SignLandmark SignLandmark SignLandmark

TABLE 21 Exemplary Message Without Segment Descriptor (Not First Messagein Edge Segment) Version = 1.0 # of objects = 4 Size = 320 B SegmentID== one SegmentID already contained in SegmentsInEH List Version = 1.0SegmentsInEH 3 DrivablePathSpline 32 DrivablePathProfile 25 SignLandmark3 SegmentsInEH [3 segmentIds] DrivablePathSpline [32 control points, and36 knots] DrivablePathProfile [25 v_spline_coordinate and 25 Ave80Speed]SignLandmark SignLandmark

TABLE 22 Exemplary Control message - segments in EH update. Version =1.0 # of objects = 3 Size = 96 B SegmentID == one SegmentID alreadycontained in SegmentsInEH List SegmentsInEH 3 SegmentsInEH [3segmentIds]

Map Localization for Control

The disclosed systems and methods may use a “localization for control”engine to use the map elements to provide sampled road geometry and lanesemantics data in a close range of a vehicle. This localizationinformation may be used to improve the control application byunderstanding the road ahead. This disclosure describes exemplary logicand process for “Localization for control” (which may also be referredherein as “L4C”) by which building blocks may be extrapolated from themap and transferred via the relevant protocols described herein. The L4Clocalization control interface may transmit map elements that arerelatively close to the vehicle location in the vehicle coordinatesystem (e.g., having the center of the vehicle or the center of theonboard lidar system as the origin). In some embodiments, the L4Cprotocol may communicate road segments geometry data categorized intodifferent types, which may include 3D sampled points over splines, laneassignment data, split/merge points, stop points, or the like, or acombination thereof. These technologies may form a complementary setapplicable to different vehicle control applications that rely on mapdata and drive profile.

In some embodiments, the L4C may create an abstraction of the roadgeometry to reveal the structure of the lanes in the route ahead. TheL4C may be enabled by default but it may be dependent on localization ofthe vehicle in the Roadbook to calculation and output of data. Runningat frame rate [36 Hz], the L4C protocols may provide a basic logic forcustomer functions such as lane keeping, lane change, and other ACCfunctions. However, in some instances, since the protocol's map contentmay be limited, it may be recommended to use the EH output protocol forcomplicated implementations.

In some embodiments, the algorithmic element may be output as two ormore interfaces with a supplementary protocol designed to reducebandwidth consumption. For example, road edges, lane marks and drivablepaths' traces may be sent as a subset of sampled, three-dimensionalpoints (taken from the map-sourced splines), which may be separated intotwo protocols to enable higher flexibility on the SPI level. In someembodiments, using pre-defined configurable parameters, the amount ofcovered lanes may be varied by changing at least one of: the distancethe points grid should cover, the number of lanes to output,enable/disable lane marks and/or road edges data, the number of pointson each spline, the distance between the points, enablingspeed-dependent distance between points, or points filtering as afunction of the angle between consecutive points. For close laneassignment, the protocol may provide a description of the lanes map (atree) in a predetermined number of (e.g., 50) meters ahead of thevehicle.

Sampled Splines Points

Map points may include 3D sampled points of the DP, LM, and RE splinesin the vehicle coordinate system. The points output may be based on apredefined set of properties which may be configurable in thedevelopment phase. Each project can determine the number of splines(lanes) to sample and output. For example, using this information anadequate configuration may be saved for:

maxNumofDPs=5 [0,5]

maxNumofLMs=6 [0,6]

maxNumofREs=4 [0,4]

Each spline with its successor splines may be described by a maximum of30 points. Table 26 illustrates an exemplary control points object.

TABLE 26 Control Points Object Description Control Point [ ] X (uint16)Y (uint16) Z (uint16) Attribute_ 1 (uint8) Attribute_2 (uint8)

In some embodiments, in case the number of points limit was reached (asset by the points configuration) the logic of discarding excess pointsmay discard the highest index and sub index first (no priority forleft/right indexes), while spline type priority to discard may be asfollows: 1. RE; 2. LM; and 3. DP.

Attribute fields may store semantic data which varies according to thetype of spline the points may be representing. Table 27 illustratesexemplary attribute fields.

TABLE 27 Exemplary Attribute Fields Point Drivable Dataset type PathLane Mark Road Edge Attribute_1 [8bit] MSB LSB MSB LSB Common [1bit][1bit] [1bit][5bit] [1bit] [1bit] [1bit][5bit] speed [kph] [Cha.][Start] [End] [Cha.] [Start] [End] [Type] [Type] Attribute_2 [8bit][4bit] [4bit] None None [color] [width] None

The description of the attributes are as follows:

“Cha.”—changed, may indicate if the presented 3D point has indicated achange in one of the following parameters: “Start”—may be point may bestart of spline; “End”—may be point may be end of spline; “Type”—type ofLM/RE; “Color”—color of Lane mark; and Width—width of lane markings in10 cm bins.

“Common speed”—For drivable path type points, the Attribute_1 value maystore the measured crowd speed in km/h.

The points may be output to the protocol according to the configurationdescribed elsewhere in this disclosure. The order of buffer utilizationmay be ego, neighbors, and neighbors of neighbors, which may includevarious types such as drivable paths, lane marks, road edges, etc. Eachset of points may be output with additional information that coversspline ID, type, indexes, sub-index driving direction, lane left/rightindication, or the like, or a combination thereof. In some embodiments,if Ego is 0, the left lanes may be −1 and −2, and the right lanes may be1, 2, 3, etc.

Splines Indexing

The Roadbook may cover all types of scenarios (including junctions)where a spline can split into multiple splines heading in differentdirections. Information on consecutive splines and their IDs may begiven in the sub-indexes, which may be part of every spline description.

FIG. 38 is a schematic diagram of exemplary indexes and sub-indexes ofsplines associated with road segments, consistent with disclosedembodiments. As illustrated in FIG. 38, the vehicle may be driving inthe center of the main road and it may be located in a current frame. Itmay receive information on the lanes forming the road to the right (DP,LM, RE) and also on splits that may be further ahead. The limitation maybe the distance and/or the max number of points that could betransmitted (whichever may be reached first).

In some embodiments, every spline may include at least one of an index,a sub-index, or a left/right indication, which may provide informationrelating to the location of the lane relative to the vehicle position(as illustrated in FIG. 38). All the splines belonging to a lane may getthe index of the lane. Landmarks that might be related to two lanes mayget the index of one of these lanes. In some embodiments, in case RE isnot adjacent to an indexed lane it may be assigned with the max indexconfigured. For example, assuming that there are 4 lanes on the left ofthe ego lane (i.e., the lane in which the host vehicle travels) and only3 dps may be configured (0, 1, −1), the left RE will be assigned index−1L.

In some embodiments, in a case of lane split, the index of the forkedlanes and their relative splines may remain the same as the originallanes from where they forked, but a different sub-index indicates thefork. For example, as illustrated in FIG. 38, lane index 1 forks intotwo lanes. Both lanes get index 1, but the right one gets sub-index 2and the left one gets sub-index 1. In FIG. 38m , the digits indicatingthe sub-index value may be the ones after the decimal point, but in theactual protocol the index and sub-index may be two separate fields. The“index” and sub-index may describe a lane indexing, so all of thesplines of the same lane may get the same index and sub-index. If a lanewith sub-index 2 also forks into two lanes their index remains 1, butone of them may get sub-index 21 and the other may get sub-index 22.

In some embodiments, a left/right indication, which may indicate whetherthe spline borders the lane at its right or left side, may be used forroad edge and lane mark splines. FIG. 39 is a schematic diagram of anexemplary DP projection of 10 points per spline in set distances of apredetermined number of meters, consistent with disclosed embodiments.

In some embodiments, when the dynamic distance between points areenabled, the distance between points be determined, as follows:

1) At speed=0 the distance=distanceBetweenPoints; or

2) At speeds>0 the distance will follow Equation (1) below:

$\begin{matrix}{{{Distance} = {{Minimum}\left( {{{dist} + {\frac{{maxDist} - {dist}}{30}*{egospeed}}},{maxDist}} \right)}},} & (1)\end{matrix}$

where dist=distanceBetweenPoints, maxDist=maxDistanceBetweenPoints.Secondary lanes may behave similarly; i.e.,dist=distanceBetweenPointsSecondaryLanes. FIG. 40 is a schematic diagramillustrating exemplary relationship between the dynamic distance betweenpoints and the ego speed, consistent with disclosed embodiments.

Lane Assignment

The lane assignment message may describe the topology between lanes as atree structure, where the root of the tree may be the location of thevehicle in the ego lane. The message may include a list of lanes, whereeach lane may include a sub list of right neighbor lanes, left neighborlanes, and a sub list of successor lanes. In some embodiments, the laneassignment message may also include a Boolean indication of whether thislane is in the driving direction or an oncoming lane. In someembodiments, the data in the protocol itself may be not sorted in anyform except for the actual objects. Nevertheless, each lane may containenough information to correctly position it on the illustrated mapsurface.

Table 28 illustrates an exemplary Lane Object.

TABLE 28 Lane Object Description Names Description Data egoDpId(int) DPID of the ego lane Types uInEgoId(float) “U” value on the DP spline ofthe vehicle location of the ego lane LateralDistFromDp(float) Lateraldistance from DP on ego lane (right negative, left positive) Lanes dpId(int) DP ID of this lane distanceFromVehicle(float) Driving distance ofthe start of this lane from the vehicle length(float) Length of lane inmeters leftNeighborsIds[int] Const max size of 3. A value “−1” indicatesthere may be no spline in that index. rightNeighborsIds[int] Const maxsize of 3. A value “−1” indicates there may be no spline in that index.successorsIds[int] Const max size of 3. A value “−1” indicates there maybe no spline in that index. isOncomig(bool) “True” if lane may beopposite to vehicle driving direction meanWidth Lane width in meters

Drivable Path Stop Points

Stop lines in front of intersections or crosswalks may be mapped in theRoadbook and indicated by a line in front of the stop line. The DPspline and the stop line marking may share the 3D intersection points inthe protocol. Table 29 illustrates an exemplary DP Stop Points Object.

TABLE 29 DP Stop Points Object Description DP Stop Point Spline ID(uint8) X (uint16) Y (uint16) Z (uint16)

Intersection Points

Intersection points may be a description common to both merge and splitpoints. In some embodiments, since these points have similar attributes,it may be possible to use the same object to describe them. Merge/splitpoints may be semantic information nodes that may be calculated andpresented at the DP intersections/splits. In common with other RB data,this information may be derived from the most common driving profile.The configuration of the relevant distance for which a project requiresthis projection, and the amount of points to be presented, may bepre-defined using the following properties: enableStopPoints[bool]—True/False; and stopPointsDistance [uint]—Max val. may be 200meters. Table 30 illustrates an exemplary Intersection Points Object.FIG. 41 is a schematic diagram of exemplary drivable path merge points,consistent with disclosed embodiments.

TABLE 8 Intersection Points Object Description Merge Point/SplitnumDpsIn(uint8) Point numDpsOut(uint8) X (uint16) Y (uint16) Z (uint16)DPid_0(uint8) DPid_1(uint8) DPid_2(uint8) DPid_3(uint8)

Electronic Horizon: Variable Header and Object Payload

As described elsewhere in this disclosure, at least one EH processor ofa host vehicle may be programmed to receive map data (e.g., REM maptiles from server 2701) and receive output provided by one or morevehicle sensors (e.g., a motion sensor, a camera, a GPS device, etc.).The EH processor may also be programmed to localize the vehicle anddetermine an appropriate electronic horizon for the vehicle (includinge.g., where the vehicle may travel in a predetermined time of period)based on the received map data and the output provided by the sensor(s).In some embodiments, the electronic horizon may also include mapinformation for a particular location or region. Additionally,determining am electronic horizon may include a consideration of thegeometry of a vehicle's path to determine the density of the points toprovide a vehicle (e.g., direct or straight-line routes may not need asmany points) and may provide denser information for an ego-lane where avehicle travels as opposed to neighboring lanes. Further the electronichorizon may include information about speed (e.g., average speed orlegal speed), the type of lane etc.

By way of example, referring back to FIG. 35, an EH processor of vehicle3501 may determine the location of vehicle 3501 relative to the mapbased on output provided by one or more onboard sensors. The EHprocessor may also determine an area 3502 (i.e., an electronic horizon)covering the road segments that the vehicle may travel in, for example,10 seconds. In some embodiments, the EH processor may also generate anavigation information packet including information associated with thedetermined electronic horizon (e.g., area 3502 illustrated in FIG. 35).The EH processor may further transmit the navigation information packetto a navigation system processor, which may be responsible for bufferingand updating the received electronic horizon information based as newnavigation packets are received from the EH processor. The navigationsystem processor may also cause the host vehicle to navigate based onthe received navigation information packet.

In some embodiments, the navigation information packet generated by theEH processor may include a header portion and a payload portion. By wayof example, Table 5, discussed earlier, illustrates an exemplarynavigation information packet, which may be in the form of a messagecommunicated between an EH processor and a navigation system processoraccording to the EH protocol described elsewhere in this disclosure. Thenavigation information packet may include a header portion and a payloadportion. As described elsewhere in this disclosure, the header portionmay be used as a dictionary for parsing the payload portion andre-constructing the EH data. For example, the header portion may specifywhat information is included in the payload portion. By way of example,Table 6, discussed earlier, illustrates an exemplary header portion of anavigation information packet. The header portion may includeinformation such as the number of the object(s) included in the payloadportion, the size of each of the object(s), the segment ID associatedwith each of the object(s), the description of each of the object(s),etc.

The payload portion may include information relating to each of theobject(s) associated with the road segments in the EH. As describedelsewhere in this disclosure, the object(s) specified in the payloadportion may include one or more map elements associated with the roadsegments, such as a spline object (e.g., a lane mark, a road edge, adrivable path, etc.), a pole object, a traffic sign object, anumber-of-lanes object (indicating the number of the lanes associatedwith a road segment), relation between splines and lanes (which splinebelongs to which lane), a drivable path profile, an average speed and/orlegal speed in points on a drivable path spline, lane mark change points(one lane mark type changed to another lane mark type), a lane markcolor (which may include paired information about the changed lanemark), road edge type change points, a lane topology, and lane borders.For example, the payload portion may include a spline object (e.g., adrivable path, a road edge, a lane mark, etc.). A spline object mayinclude a predetermined set of points relative to the spline, including,for example, points on a drivable path at predetermined intervals aheadof a current host vehicle position (e.g., 1 second, 2 seconds, 5seconds, etc., ahead of the current host vehicle position). The same maybe true for drivable paths in adjacent lanes. The predeterminedintervals may be selectable or may vary with the vehicle speed. Detaileddescriptions of information relating to an object included in thepayload portion have been provided elsewhere in this disclosure, whichare not repeated here for brevity purposes.

In some embodiments, the size of the payload portion of the navigationinformation packet may vary, depending on the object(s) included in thepayload portion. For example, as illustrated in Table 5, discussedearlier, the payload portion of the navigation information packet inthis example may include three objects. The payload portion of adifferent navigation information packet may include more or lessobjects, the size of the payload portion of that navigation informationpacket may be different from the payload portion illustrated in Table 5.One of the advantages of having a variable-sized payload portion (and avariable-sized navigation information packet) is to provide flexibilityand to reduce the processing cost. A standardized navigation packetdesigned to cover all possible scenarios and encountered objects may behuge, and most fields would be empty during update events. The disclosedsystems and methods provide solutions for communicating the electronichorizon information (e.g., drivable paths, encountered objects andpositions, etc.) in a packet customized to convey the relevantinformation.

FIG. 42 illustrates exemplary components of vehicle 2702 configured toperform the functions thereof described herein. As illustrated in FIG.42, vehicle 2702 may include one or more electronic horizon processor4210, one or more navigation system processors 4220, a memory 4230, astorage device 4240, a communications port 4250, one or more sensors4260, a lidar system 4270, and a navigation system 4280.

Electronic horizon processor 4210 may be configured to perform one ormore functions of an EH processor (and/or the EH constructor) describedin this disclosure. Electronic horizon processor 4210 may include amicroprocessor, preprocessors (such as an image preprocessor), agraphics processing unit (GPU), a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices suitable for running applications or performing acomputing task. In some embodiments, electronic horizon processor 4210may include any type of single or multi-core processor, mobile devicemicrocontroller, central processing unit, etc. Various processingdevices may be used, including, for example, processors available frommanufacturers such as Intel®, AMD®, etc., or GPUs available frommanufacturers such as NVIDIA®, ATI®, etc. and may include variousarchitectures (e.g., x86 processor, ARM®, etc.). Any of the processingdevices disclosed herein may be configured to perform certain functions.Configuring a processing device, such as any of the described processorsor other controller or microprocessor, to perform certain functions mayinclude programming of computer-executable instructions and making thoseinstructions available to the processing device for execution duringoperation of the processing device. In some embodiments, configuring aprocessing device may include programming the processing device directlywith architectural instructions. For example, processing devices such asfield-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), and the like may be configured using, for example, oneor more hardware description languages (HDLs).

In some embodiments, electronic horizon processor 4210 may includecircuitry 4211 and memory 4212. Memory 4212 may store instructions that,when executed by circuitry 4211, may cause electronic horizon processor4210 to perform the functions of electronic horizon processor 4210described herein. Circuitry 4211 may include any one or more of theexamples described herein.

Navigation system processor 4220 may be configured to perform one ormore functions of a navigation system processor (and/or an EHreconstructor) described in this disclosure. In some embodiments,navigation system processor 4220 may include circuitry 4221 and memory4222. Memory 4222 may store instructions that, when executed bycircuitry 4221, may cause navigation system processor 4220 to performthe functions of navigation system processor 4220 described herein.Circuitry 4221 may include any one or more of the examples describedherein.

Vehicle 2702 may include a memory 4230 that may store instructions forvarious components of vehicle 2702. For example, memory 4230 may storeinstructions that, when executed by electronic horizon processor 4210(and/or navigation system processor 4220), may be configured to causeelectronic horizon processor 4210 (and/or navigation system processor4220) to perform one or more functions described herein. Memory 4230 mayinclude any number of random-access memories, read-only memories, flashmemories, disk drives, optical storage, tape storage, removable storage,and other types of storage. In one instance, memory 4230 may be separatefrom electronic horizon processor 4210 and/or navigation systemprocessor 4220. In another instance, memory 4230 may be integrated intoelectronic horizon processor 4210 and/or navigation system processor4220. In some embodiments, memory 4230 may include software forperforming one or more computing tasks, as well as a trained system,such as a neural network, or a deep neural network.

Storage device 4240 may be configured to store various data andinformation for one or more components of vehicle 2702. Storage device4240 may include one or more hard drives, tapes, one or more solid-statedrives, any device suitable for writing and read data, or the like, or acombination thereof. Storage device 4240 may store map data, including,for example, data of one or more map segments, which may be accessed byelectronic horizon processor 4210 and/or navigation system processor4220. In some embodiments, storage device 4240 may store a map database.Electronic horizon processor 4210 may retrieve data of one or more mapsegments from the map database. For example, electronic horizonprocessor 4210 and/or navigation system processor 4220 may retrieve mapdata associated with a map segment.

Communications port 4250 may be configured to facilitate datacommunications between vehicle 2702 and one or more components of thedisclosed systems described herein via a network. For example,communications port 4250 may be configured to receive data from andtransmit data to a server via one or more public or private networks,including the Internet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like.

Sensor 4260 may be configured to collect information relating to vehicle2702 and/or the environment of vehicle 2702. Sensor 4260 may include oneor more of an image sensor (e.g., a camera), a radar device, a lidardevice, a speed sensor, an acceleration sensor, a brake sensor, asuspension sensor, a positioning device (e.g., a Global PositioningSystem (GPS) device), an accelerometer, a gyro sensor, a speedometer, orthe like, or a combination thereof. For example, vehicle 2702 mayinclude an image sensor (e.g., a camera) configured to capture one ormore images of its environment, which may include representation of anobject (or at least a portion thereof). In some embodiments, vehicle2702 may include one or more image sensors similar to image capturedevice 122, image capture device 124, and/or image capture device 126described elsewhere in this disclosure. As another example, vehicle 2702may include a GPS device configured to collect positioning dataassociated with positions of vehicle 2702 over a period of time.

LIDAR system 4270 may include one or more LIDAR units. In someembodiments, the one or more LIDAR units may be positioned on a roof ofvehicle 2702. Such a unit may include a rotating unit configured togather LIDAR reflection information within a 360-degree field of viewaround vehicle 2702 or from any sub-segment of the 360-degree field ofview (e.g., one or more FOVs each representing less than 360 degrees).In some embodiments, a LIDAR unit may be positioned at a forwardlocation on vehicle 2702 (e.g., near the headlights, in the front grill,near the fog lamps, in a forward bumper, or at any other suitablelocation). In some cases, one or more LIDAR units installed on a forwardportion of vehicle 2702 may collect reflection information from a fieldof view in an environment forward of vehicle 2702. The data collected byLIDAR system 4270 may be provided to electronic horizon processor 4210.Alternatively or additionally, the data may be transmitted to a serverand/or a database via a network, as described elsewhere in thisdisclosure.

Any suitable type of LIDAR unit may be included on vehicle 2702. In somecases, LIDAR system 4270 may include one or more flash LIDAR units(e.g., 3D flash LIDAR) where an entire LIDAR field of view (FOV) isilluminated with a single laser pulse, and a sensor including rows andcolumns of pixels to record returned light intensity and time offlight/depth information. Such flash systems may illuminate a scene andcollect LIDAR “images” multiple times per second. Scanning LIDAR unitsmay also be employed. Such scanning LIDAR units may rely on one or moretechniques for dispersing a laser beam over a particular FOV. In somecases, a scanning LIDAR unit may include a scanning mirror that deflectsand directs a laser beam toward objects within the FOV. Scanning mirrorsmay rotate through a full 360 degrees or may rotate along a single axisor multiple axes over less than 360 degrees to direct the laser toward apredetermined FOV. In some cases, LIDAR units may scan one horizontalline. In other cases, a LIDAR unit may scan multiple horizontal lineswithin an FOV, effectively rastering a particular FOV multiple times persecond.

The LIDAR units in LIDAR system 4270 may include any suitable lasersource. In some embodiments, the LIDAR units may employ a continuouslaser. In other cases, the LIDAR units may rely upon pulsed laseremissions. Additionally, any suitable laser wavelength may be employed.In some cases, a wavelength of between about 600 nm to about 1000 nm maybe used.

The LIDAR unit(s) in LIDAR system 4270 may also include any suitabletype of sensor and provide any suitable type of output. In some cases,sensors of the LIDAR units may include solid state photodetectors, suchas one or more photodiodes or photomultipliers. The sensors may alsoinclude one or more CMOS or CCD devices including any number of pixels.These sensors may be sensitive to laser light reflected from a scenewithin the LIDAR FOV. The sensors may enable various types of outputfrom a LIDAR unit. In some cases, a LIDAR unit may output raw lightintensity values and time of flight information representative of thereflected laser light collected at each sensor or at each pixel orsub-component of a particular sensor. Additionally or alternatively, aLIDAR unit may output a point cloud (e.g., a 3D point cloud) that mayinclude light intensity and depth/distance information relative to eachcollected point). LIDAR units may also output various types of depthmaps representative of light reflection amplitude and distance to pointswithin a field of view. LIDAR units may provide depth or distanceinformation relative to particular points within an FOV by noting a timeat which light from the LIDAR's light source was initially projectedtoward the FOV and recording a time at which the incident laser light isreceived by a sensor in the LIDAR unit. The time difference mayrepresent a time of flight, which may be directly related to the roundtrip distance that the incident laser light traveled from the lasersource to a reflecting object and back to the LIDAR unit. Monitoring thetime of flight information associated with individual laser spots orsmall segments of a LIDAR FOV may provide accurate distance informationfor a plurality of points within the FOV (e.g., mapping to even verysmall features of objects within the FOV). In some cases, LIDAR unitsmay output more complex information, such as classification informationthat correlates one or more laser reflections with a type of object fromwhich the laser reflection was acquired.

Navigation system 4280 may be configured to assist a driver of vehicle2702 to operate vehicle 2702. Alternatively or additionally, navigationsystem 4280 may include an autonomous vehicle navigation systemconfigured to control the movement of vehicle 2702 as describedelsewhere in this disclosure. For example, navigation system 4280 may beconfigured to control vehicle 2702 based on instructions received fromnavigation system processor 4220. By way of example, navigation systemprocessor 4220 may cause navigation system 4280 to control vehicle 2702to navigate along a target trajectory based on the navigationinformation packet received from electronic horizon processor 4210.

In some embodiments, an electronic horizon processor 4210 and anavigation system processors 4220 may be integrated into one processorconfigured to perform the functions of the electronic horizon processorand the navigation system processor described in this disclosure.

FIG. 43 is a flowchart showing an exemplary process 4300 for navigatinga vehicle, consistent with disclosed embodiments. One or more steps ofprocess 4300 may be performed by a vehicle (e.g., vehicle 2702), adevice (e.g., vehicle device 2703) associated with the host vehicle, atleast one processor (e.g., an electronic horizon processor), and/or aserver (e.g., server 2701). While the descriptions of process 4300provided below use vehicle 2702 (or one or more components thereof) asan example, one skilled in the art would appreciate that one or moresteps of process 4300 may be performed by a server and/or a vehicledevice. For example, in an embodiment, a system for navigating a vehicleincludes at least one processor (e.g., an electronic horizon processor)comprising circuitry and a memory. The memory includes instructions thatwhen executed by the circuitry cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 43. As another example, in anotherembodiment, a non-transitory computer readable medium containsinstructions that, when executed by at least one processor (e.g., anelectronic horizon processor), may cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 43.

At step 4301, at least one electronic horizon processor (e.g.,electronic horizon processor 4210) accesses a map representative of atleast a road segment on which the host vehicle travels or is expected totravel. For example, the at least one electronic horizon processor maybe programmed to receive a map from storage device 4240 and/or memory4230. Alternatively or additionally, the at least one electronic horizonprocessor may receive the map data from a server (e.g., server 2701)and/or a database (e.g., database 2704), which may be remotely locatedrelative to vehicle 2702, via network 2705. In some embodiments, the mapaccessed by the at least one electronic horizon processor may representa sub-segment of a larger map available on a map server (e.g., server2701) remotely located relative to the host vehicle.

In some embodiments, the accessed map may include a plurality ofthree-dimensional splines associated with one or more road segments, andthe three-dimensional splines may represent one or more of: a drivablepath for the host vehicle, a road edge, or a lane mark.

In some embodiments, the accessed map is generated based oncrowd-sourced drive information collected from a plurality of vehiclesthat traversed the road segment on which the host vehicle travels or isexpected to travel prior to the host vehicle (as described elsewhere inthis disclosure).

In some embodiments, the accessed map may include a plurality ofthree-dimensional point locations specified in a map coordinate systemassociated with the accessed map (e.g., a geographic coordinate systemsuch as GPS coordinate system). One or more of the objects included inthe payload portion of the navigation packet may include athree-dimensional point location specified in a coordinate systemassociated with the host vehicle (e.g., a coordinate system local to thehost vehicle). For example, the coordinate system associated with thehost vehicle may have the position of one of the one or more cameras ofthe host vehicle (or the center of the host vehicle) as the origin ofthe coordinate system.

At step 4302, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) receives an output provided by atleast one vehicle sensor (e.g., one or more sensors 4260). In someembodiments, the at least one vehicle sensor may include one or morecameras configured to capture images of an environment of the hostvehicle. For example, vehicle 2702 may include one or more camerassimilar to image capture device 122, image capture device 124, and imagecapture device 126 described above, which may be configured to captureimages of an environment of the host vehicle. The received output mayinclude at least one image captured by the one or more cameras.

In some embodiments, the at least one vehicle sensor may also includeother types of sensors, including, for example, a speed sensor, anacceleration sensor, a GPS receiver, a radar sensor, a LIDAR sensor, orthe like, or a combination thereof.

At step 4303, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) localizes the host vehicle relativeto the map based on analysis the at least one image captured by the oneor more cameras. For example, the at least one electronic horizonprocessor may determine the position of the host vehicle relative to themapped drivable path based on an identification of one or more mappedlandmarks represented in the at least one captured image and acomparison of an image position of the one or more mapped landmarks inthe at least one captured image with an expected image position of theone or more mapped landmarks associated with at least one position alongthe drivable path. In some embodiments, the mapped drivable path isrepresented in the map as a three-dimensional spline. Alternatively oradditionally, the at least one electronic horizon processor maydetermine a position of the host vehicle relative to a mapped drivablepath based on an identification of one or more mapped landmarksrepresented in two or more captured images and based on a motion historyof the host vehicle during a time period between when the two or morecaptures images were acquired. Alternatively or additionally, the atleast one electronic horizon processor may localize the host vehiclerelative to the map based on other methods or processes for localizing avehicle described herein (e.g., the methods described earlier inconnection with FIGS. 25A, 25B, 25C, and 25D).

At step 4304, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines an electronic horizon forthe host vehicle based on the localization of the host vehicle relativeto the map. For example, referring back to FIG. 35, the at least oneelectronic horizon processor of vehicle 2702 (which may be similar tovehicle 3501 illustrated in FIG. 35) may determine the position ofvehicle 2702 relative to the map illustrated in FIG. 25 and determine anarea 3502 covered by an electronic horizon radius based on the relativeposition of vehicle 2702.

In some embodiments, the at least one electronic horizon processor maydetermine the electronic horizon, at least in part, based on the currentspeed of the host vehicle and/or the travel direction of the hostvehicle. For example, the at least one electronic horizon processor mayobtain the current speed provided by an onboard speed sensor (which maybe one of the at least one vehicle sensor). The at least one electronichorizon processor may determine the electronic horizon based on thecurrent speed of the host vehicle. For example, the at least oneelectronic horizon processor may determine the electronic horizon thatis associated with a portion of the map accessible to the host vehiclewithin a predetermined time interval under the current speed. Thepredetermined time interval may be in a range of 0.1 seconds to 10minutes. In some embodiments, the predetermined time interval may be upto 1 second, 2 seconds, 5 seconds, 10 seconds, or 1 minute.Alternatively or additionally, the at least one electronic horizonprocessor may determine the electronic horizon that is associated with apredetermined spatial envelope around the host vehicle (e.g., one ormore envelopes described earlier in connection with FIGS. 28A, 28B, 28C,28D, 28E, 28F, 28G, and 28H).

At step 4305, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) generates a navigation informationpacket including information associated with the determined electronichorizon. For example, referring to FIG. 35, the at least one electronichorizon processor may obtain map information relating to the roadsegment(s) included in area 3502 (i.e., the electronic horizondetermined at step 4204) and generate a navigation information packetincluding the information relating to one or more objects associatedwith the road segment(s). The navigation information packet may includea header portion and a payload portion. The header portion may specifywhat information is included in the payload portion. The payload portionmay include information relating to one or more objects associated withthe road segment(s). In some embodiments, the payload portion may bevariable-sized.

In some embodiments, the header portion of the navigation informationpacket may include information identifying objects included in thepayload portion, a size of each of the objects included in the payloadportion, an indication of a number of objects included in the payloadportion, or the like, or a combination thereof. By way of example, Table5 and Table 6, discussed above, illustrate various types of informationthat may be included in a header portion, the detailed descriptions ofwhich are repeated here for brevity purposes.

The payload portion may include one or more objects associated with theroad segment(s) included in the determined electronic horizon. Theobjects included in the payload portion may include one or more of: aspline object, a drivable path profile object, a lane mark changedpoints object, a road edge changed point object, a sign landmark object,a pole landmark object, a lane topology object, a drivable path borderobject, a lane merge point object, a lane split point object, or areference point change event object. In some embodiments, one or moreobjects included in the payload portion may be generated by the at leastone electronic horizon processor (e.g., electronic horizon processor4210) based on the determined electronic horizon. For example, one ormore objects included in the payload portion may be generated by the atleast one electronic horizon processor (e.g., electronic horizonprocessor 4210) based on features associated with the accessed map thatare included within the determined electronic horizon.

In some embodiments, the at least one electronic horizon processor(e.g., electronic horizon processor 4210) may be configured to generatean updated navigation information packet after detecting a change in oneor more (or a group) of mapped features implicated by the determinedelectronic horizon for the host vehicle. For example, the at least oneelectronic horizon processor may detect a change in the position of atraffic sign (i.e., an object included in the electronic horizon) basedon the image analysis of one or more images captured by an onboardcamera. The at least one electronic horizon processor may generate anupdated navigation information packet based on the detected change inthe position of the traffic sign.

At step 4306, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) outputs the generated navigationinformation packet to one or more navigation system processors (e.g.,navigation system processor 4220). The one or more navigation systemprocessors may be configured to cause the host vehicle to execute atleast one navigational maneuver based on the information included in thenavigation information packet. For example, one or more navigationsystem processors may be configured to cause navigation system 4280 toexecute at least one navigational maneuver based on the informationincluded in the navigation information packet (e.g., a drivable pathalong a road segment). The at least one navigational maneuver includesone or more of: maintaining a current speed of the host vehicle;maintaining a current heading direction of the host vehicle; changing aheading direction of the host vehicle; changing a speed of the hostvehicle; accelerating the host vehicle; or braking the host vehicle.

Electronic Horizon: Edge Segmentation Between Road Nodes

As described elsewhere in this disclosure, one or more electronichorizon processors may provide one or more navigation informationpackets including map and/or navigational information to one or morenavigation system processors. The one or more navigation systemprocessors may cause a host vehicle to navigate based on the providedmap and/or navigational information. In some cases, a navigation packetcovering an entire road segment (which may also be referred herein as anedge) between nodes (e.g., roundabouts, intersections, merges, splits,etc.) may be large, as a road segment may extend for fairly longdistances between such nodes, and many landmarks and other objectstypically included in the navigation packet may be encountered. Such anapproach may place a memory burden on both the constructor side (i.e.,the electronic horizon processor) and the re-constructor side (i.e., thenavigation system processor). The disclosed systems and methods maydivide an edge into more manageable sub-segments. For example, eachsub-segment may include, for example, about 100 meters of a road edgebetween nodes. By way of example, at least one electronic horizonprocessor may generate a navigation information packet and transmit thenavigation information packet to a navigation system processor. Asdescribed elsewhere in this disclosure, a navigation information packetmay include a SegmentInEH object (e.g., SegmentsInEH object 3321illustrated in FIG. 33), which may be a vector of segmentIDs (e.g.,32-bit unsigned integer each) listing the map segment IDs (and/or one ormore subsegment IDs) that are covered in the potential travel envelopeof vehicle 2702. The SegmentsInEH object may indicate when a certainroad edge subsegment can be deleted from a buffer (e.g., when the roadedge subsegment is no longer included in the electronic horizon list ofaccessible road subsegments). For example, vehicle 2702 (or a componentthereof) may delete from its local memory data associated withSegmentsInEH that is not included in the list.

FIG. 44 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments. One or more steps ofprocess 4400 may be performed by a vehicle (e.g., vehicle 2702), adevice (e.g., vehicle device 2703) associated with the host vehicle, atleast one processor (e.g., an electronic horizon processor), and/or aserver (e.g., server 2701). While the descriptions of process 4400provided below use vehicle 2702 (or one or more components thereof) asan example, one skilled in the art would appreciate that one or moresteps of process 4400 may be performed by a server and/or a vehicledevice. For example, in an embodiment, a system for navigating a vehicleincludes at least one processor (e.g., an electronic horizon processor)comprising circuitry and a memory. The memory includes instructions thatwhen executed by the circuitry cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 44. As another example, in anotherembodiment, a non-transitory computer readable medium containsinstructions that, when executed by at least one processor (e.g., anelectronic horizon processor), may cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 44.

At step 4401, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) accesses a map representative of aroad (or a road segment) on which the host vehicle travels or isexpected to travel. In some embodiments, step 4401 may be similar tostep 4301 of process 4300 described above. For example, the at least oneelectronic horizon processor may be programmed to receive a map fromstorage device 4240 and/or memory 4230. Alternatively or additionally,the at least one electronic horizon processor may receive the map datafrom a server (e.g., server 2701) and/or a database (e.g., database2704), which may be remotely located relative to vehicle 2702, vianetwork 2705. In some embodiments, the map accessed by the at least oneelectronic horizon processor may represent a sub-segment of a larger mapavailable on a map server (e.g., server 2701) remotely located relativeto the host vehicle.

In some embodiments, in the map, the road may be represented as aninternode road segment between two mapped nodes, and in the map, theinternode road segment may be further divided into a plurality ofinternode road sub-segments. As described elsewhere in this disclosure,a mapped node may include a roundabout, an intersection, a lane split, alane merge (or the like) represented in a map. An edge may be a roadbetween two mapped nodes. An edge segment may be a logical unitcontaining map data. For example, an edge may be divided into aplurality of edge segments (e.g., edge segments illustrated in FIG. 35).An edge segment may be further be divided into a plurality ofsubsegments. Dividing edges into sub-segments may reduce memoryconsumption at both sides (constructor and re-constructor) since an edgesegment can be quite long in some cases. Alternatively or additionally,the accessed map may include a plurality of three-dimensional splines,which may represent one or more of: a drivable path for the hostvehicle, a road edge, or a lane mark.

In some embodiments, the accessed map is generated based oncrowd-sourced drive information collected from a plurality of vehiclesthat traversed the road segment on which the host vehicle travels or isexpected to travel prior to the host vehicle (as described elsewhere inthis disclosure).

In some embodiments, the map accessed by the at least one electronichorizon processor may represent a sub-segment of a larger map availableon a map server (e.g., server 2701) remotely located relative to thehost vehicle.

At step 4402, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) receives an output provided by atleast one vehicle sensor. In some embodiments, step 4402 may be similarto step 4302 of process 4300 described above. For example, the at leastone vehicle sensor may include one or more cameras configured to captureimages of an environment of the host vehicle. By way of example, vehicle2702 may include one or more cameras similar to image capture device122, image capture device 124, and image capture device 126 describedabove, which may be configured to capture images of an environment ofthe host vehicle. The received output may include at least one imagecaptured by the one or more cameras.

In some embodiments, the at least one vehicle sensor may also includeother types of sensors, including, for example, a speed sensor, anacceleration sensor, a GPS receiver, a radar sensor, a LIDAR sensor, orthe like, or a combination thereof.

At step 4403, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) localizes the host vehicle relativeto the map based on analysis of the at least one image captured by theone or more cameras. For example, at least one electronic horizonprocessor may localize the host vehicle relative to at least one mappedfeature (and/or at least one mapped object) based on analysis of the atleast one image captured by the one or more cameras. In someembodiments, step 4403 may be similar to step 4303 of process 4300described above.

In some embodiments, the at least one mapped feature relative to whichthe host vehicle is localized may include a drivable path for the hostvehicle represented in the map. In some embodiments, the mapped drivablepath may be represented in the map as a three-dimensional spline. The atleast one electronic horizon processor may localize the host vehiclerelative to a mapped drivable path based on analysis of the at least oneimage captured by the one or more cameras. By way of example, at leastone electronic horizon processor may determine a position of the hostvehicle relative to a mapped drivable path based on an identification ofone or more mapped landmarks represented in the at least one capturedimage and a comparison of an image position of the one or more mappedlandmarks in the at least one captured image with an expected imageposition of the one or more mapped landmarks associated with at leastone position along the drivable path. Alternatively or additionally, thelocalization of the host vehicle relative to the at least one mappedfeature may include determining a position of the host vehicle relativeto a mapped drivable path based on an identification of one or moremapped landmarks represented in two or more captured images and based ona motion history of the host vehicle during a time period between whenthe two or more captures images were acquired.

At step 4404, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines an electronic horizon forthe host vehicle based on the localization of the host vehicle. Forexample, the at least one electronic horizon processor may determine anelectronic horizon for the host vehicle based on the localization of thehost vehicle relative to the at least one mapped feature. In someembodiments, step 4404 may be similar to step 4304 of process 4300described above. For example, the at least one electronic horizonprocessor may determine the electronic horizon, at least in part, basedon the current speed of the host vehicle and/or the travel direction ofthe host vehicle. For example, the at least one electronic horizonprocessor may obtain the current speed provided by an onboard speedsensor (which may be one of the at least one vehicle sensor). The atleast one electronic horizon processor may determine the electronichorizon based on the current speed of the host vehicle. For example, theat least one electronic horizon processor may determine the electronichorizon that is associated with a portion of the map accessible to thehost vehicle within a predetermined time interval under the currentspeed. The predetermined time interval may be in a range of 0.1 secondsto 10 minutes. In some embodiments, the predetermined time interval maybe up to 1 second, 2 seconds, 5 seconds, 10 seconds, or 1 minute.Alternatively or additionally, the at least one electronic horizonprocessor may determine the electronic horizon that is associated with apredetermined spatial envelope around the host vehicle (e.g., one ormore envelopes described earlier in connection with FIGS. 28A, 28B, 28C,28D, 28E, 28F, 28G, and 28H).

At step 4405, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines a set of internode roadsub-segments that are included in the electronic horizon. For example,the at least one electronic horizon processor may obtain all internoderoad sub-segments in the map area corresponding to the electronichorizon, and select all the internode road sub-segments as the set ofinternode road sub-segments.

In some embodiments, the determined set of internode road sub-segmentsmay include two or more internode road sub-segments. Alternatively, thedetermined set of internode road sub-segments includes only oneinternode road sub-segment.

In some embodiments, the determined set of internode road sub-segmentsthat are included in the electronic horizon may include at least oneinternode road-subsegment that located only partially within an envelopeassociated with the electronic horizon.

In some embodiments, each of the plurality of internode roadsub-segments in the map may represent a section of the internode roadsegment having less than a predetermined maximum length.

At step 4406, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) generates one or more navigationinformation packets. In some embodiments, the one or more navigationinformation packets may include information associated with the set ofinternode road sub-segments included in the electronic horizon.Alternatively or additionally, the one or more navigation informationpackets may include other types of information described in thisdisclosure (e.g., a spline object, a drivable path profile object, alane mark changed points object, etc.). In some embodiments, step 4406may be similar to step 4305 of process 4300 described above.

In some embodiments, at least one navigation information packet may begenerated for each of the internode road sub-segments included in theelectronic horizon.

In some embodiments, each of the one or more navigation informationpackets may include a header portion and a variable-sized payloadportion (as described elsewhere in this disclosure). The header portionmay specify what information is included in the variable-sized payloadportion. For example, the header portion of a navigation informationpacket may identify one or more objects included in the variable-sizedpayload portion. By way of example, Table 6, discussed earlier,illustrates an exemplary header portion of a navigation informationpacket. The header portion may include information such as at least oneof the number of the object(s) included in the payload portion, the sizeof each of the object(s), the segment ID associated with each of theobject(s), the description of each of the object(s), etc. In someembodiments, the object(s) included in the variable-sized payloadportion may be generated by the at least one processor based on thedetermined electronic horizon. For example, the object(s) included inthe variable-sized payload portion may be generated by the at least oneprocessor based on features associated with the accessed map that areincluded within the determined electronic horizon. In some embodiments,the object(s) included in the variable-sized payload portion include oneor more of: a spline object, a drivable path profile object, a lane markchanged points object, a road edge changed point object, a sign landmarkobject, a pole landmark object, a lane topology object, a drivable pathborder object, a lane merge point object, a lane split point object, ora reference point change event object.

In some embodiments, the one or more navigation packets may include aroad sub-segment list object representative of the internode roadsub-segments included in the determined electronic horizon. In someembodiments, changes in the road sub-segment list object map prompt oneor more navigation system processors (e.g., navigation system processor4220) to delete information associated with road sub-segments no longerincluded in a received road sub-segment list object. For example, the atleast one electronic horizon processor may transmit to the navigationsystem processor a navigation information packet including an updatedsegmentIDs list. The navigation system processor may delete anysub-segment having a segmentID that is not included in the list and wasalready received from one or more previous navigation informationpackets.

At step 4407, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) outputs the generated one or morenavigation information packets to one or more navigation systemprocessors configured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet. In some embodiments, step 4407 may besimilar to step 4306 of process 4300 described above. For example, oneor more navigation system processors may be configured to causenavigation system 4280 to execute at least one navigational maneuverbased on the information included in the navigation information packet(e.g., a drivable path along a road segment). The at least onenavigational maneuver may include one or more of: maintaining a currentspeed of the host vehicle; maintaining a current heading direction ofthe host vehicle; changing a heading direction of the host vehicle;changing a speed of the host vehicle; accelerating the host vehicle; orbraking the host vehicle.

Electronic Horizon: Dynamic Change of Map Origin

In some cases, when a vehicle's distance from an origin point becomeslarger than a defined threshold, precision errors may produce anaccumulated bias over the distance. To avoid this type of bias, thedisclosed systems and methods may dynamically change the origin point ofa map coordinate system, and one or more map objects may be transformedin the new coordinates system accordingly. In one embodiment, at leastone electronic horizon processor may send a ReferencePointChangeEventobject to at least one navigation system processor, indicating that mapcoordinates have been changed. The previous segments of the electronichorizon may be flashed on the re-constructor side (e.g., a memory or abuffer accessible by a navigation system processor). The at least onenavigation system processor may also transmit the segments in the newcoordinate system to the navigation system processor. When the change inthe map coordinate system occurs, the localization output may change toa “not localized” state. In some embodiments, at least one electronichorizon processor and at least navigation system processor maycoordinate for communicating segments in a new coordinate systemaccording to the process illustrated in FIG. 37.

FIG. 45 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments. One or more steps ofprocess 4500 may be performed by a vehicle (e.g., vehicle 2702), adevice (e.g., vehicle device 2703) associated with the host vehicle, atleast one processor (e.g., an electronic horizon processor), and/or aserver (e.g., server 2701). While the descriptions of process 4500provided below use vehicle 2702 (or one or more components thereof) asan example, one skilled in the art would appreciate that one or moresteps of process 4500 may be performed by a server and/or a vehicledevice. For example, in an embodiment, a system for navigating a vehicleincludes at least one processor (e.g., an electronic horizon processor)comprising circuitry and a memory. The memory includes instructions thatwhen executed by the circuitry cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 45. As another example, in anotherembodiment, a non-transitory computer readable medium containsinstructions that, when executed by at least one processor (e.g., anelectronic horizon processor), may cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 45.

At step 4501, at least one electronic horizon processor (e.g.,electronic horizon processor 4210) accesses a map representative of atleast a road segment on which the host vehicle travels or is expected totravel. Points in the map may be referenced relative to an initial maporigin. In some embodiments, step 450 may be similar to step 430 ofprocess 4300 described above. For example, the at least one electronichorizon processor may be programmed to receive a map from storage device4240 and/or memory 4230. Alternatively or additionally, the at least oneelectronic horizon processor may receive the map data from a server(e.g., server 2701) and/or a database (e.g., database 2704), which maybe remotely located relative to vehicle 2702, via network 2705. In someembodiments, the map accessed by the at least one electronic horizonprocessor may represent a sub-segment of a larger map available on a mapserver (e.g., server 2701) remotely located relative to the hostvehicle.

In some embodiments, the accessed map may include a plurality ofthree-dimensional splines, wherein the three-dimensional splinesrepresent one or more of: a drivable path for the host vehicle, a roadedge, or a lane mark.

In some embodiments, the accessed map is generated based oncrowd-sourced drive information collected from a plurality of vehiclesthat traversed the road segment on which the host vehicle travels or isexpected to travel prior to the host vehicle (as described elsewhere inthis disclosure).

In some embodiments, the map accessed by the at least one electronichorizon processor may represent a sub-segment of a larger map availableon a map server (e.g., server 2701) remotely located relative to thehost vehicle.

At step 4502, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) receives an output provided by atleast one vehicle sensor. In some embodiments, step 4502 may be similarto step 4302 of process 4300 described above. For example, the at leastone vehicle sensor may include one or more cameras configured to captureimages of an environment of the host vehicle. By way of example, vehicle2702 may include one or more cameras similar to image capture device122, image capture device 124, and image capture device 126 describedabove, which may be configured to capture images of an environment ofthe host vehicle. The received output may include at least one imagecaptured by the one or more cameras.

In some embodiments, the at least one vehicle sensor may also includeother types of sensors, including, for example, a speed sensor, anacceleration sensor, a GPS receiver, a radar sensor, a LIDAR sensor, orthe like, or a combination thereof.

At step 4503, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) localizes the host vehicle relativeto the map based on analysis of the at least one image captured by theone or more cameras. In some embodiments, step 4503 may be similar tostep 4303 of process 4300 described above. For example, the at least oneelectronic horizon processor may determine the position of the hostvehicle relative to the mapped drivable path based on an identificationof one or more mapped landmarks represented in the at least one capturedimage and a comparison of an image position of the one or more mappedlandmarks in the at least one captured image with an expected imageposition of the one or more mapped landmarks associated with at leastone position along the drivable path. In some embodiments, the mappeddrivable path is represented in the map as a three-dimensional spline.Alternatively or additionally, the at least one electronic horizonprocessor may determine a position of the host vehicle relative to amapped drivable path based on an identification of one or more mappedlandmarks represented in two or more captured images and based on amotion history of the host vehicle during a time period between when thetwo or more captures images were acquired. Alternatively oradditionally, the at least one electronic horizon processor may localizethe host vehicle relative to the map based on other methods or processesfor localizing a vehicle described herein (e.g., the methods describedearlier in connection with FIGS. 25A, 25B, 25C, and 25D).

At step 4504, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines an electronic horizon forthe host vehicle based on the localization of the host vehicle relativeto the map. In some embodiments, step 4504 may be similar to step 4304of process 4300 described above. For example, referring back to FIG. 35,the at least one electronic horizon processor of vehicle 2702 (which maybe similar to vehicle 3501 illustrated in FIG. 35) may determine theposition of vehicle 2702 relative to the map illustrated in FIG. 25 anddetermine an area 3502 covered by an electronic horizon radius based onthe relative position of vehicle 2702.

In some embodiments, the at least one electronic horizon processor maydetermine the electronic horizon, at least in part, based on the currentspeed of the host vehicle and/or the travel direction of the hostvehicle. For example, the at least one electronic horizon processor mayobtain the current speed provided by an onboard speed sensor (which maybe one of the at least one vehicle sensor). The at least one electronichorizon processor may determine the electronic horizon based on thecurrent speed of the host vehicle. For example, the at least oneelectronic horizon processor may determine the electronic horizon thatis associated with a portion of the map accessible to the host vehiclewithin a predetermined time interval under the current speed. Thepredetermined time interval may be in a range of 0.1 seconds to 10minutes. In some embodiments, the predetermined time interval may be upto 1 second, 2 seconds, 5 seconds, 10 seconds, or 1 minute.Alternatively or additionally, the at least one electronic horizonprocessor may determine the electronic horizon that is associated with apredetermined spatial envelope around the host vehicle (e.g., one ormore envelopes described earlier in connection with FIGS. 28A, 28B, 28C,28D, 28E, 28F, 28G, and 28H).

At step 4505, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) generates a navigation informationpacket including information associated with mapped features included inthe determined electronic horizon. In some embodiments, step 4505 may besimilar to step 4305 of process 4300 (and/or step 4406 of process 4400)described above. For example, the at least one electronic horizonprocessor may generate a navigation information packet that includes aheader portion and a payload portion. In some embodiments, the payloadportion of the navigation information packet may be variable-sized. Theheader portion may specify what information is included in the payloadportion. For example, the header portion of a navigation informationpacket may identify one or more objects included in the variable-sizedpayload portion. By way of example, Table 6, discussed earlier,illustrates an exemplary header portion of a navigation informationpacket. The header portion may include information such as at least oneof the number of the object(s) included in the payload portion, the sizeof each of the object(s), the segment ID associated with each of theobject(s), the description of each of the object(s), etc. In someembodiments, the object(s) included in the variable-sized payloadportion may be generated by the at least one processor based on thedetermined electronic horizon. For example, the object(s) included inthe variable-sized payload portion may be generated by the at least oneprocessor based on features associated with the accessed map that areincluded within the determined electronic horizon. In some embodiments,the object(s) included in the variable-sized payload portion include oneor more of: a spline object, a drivable path profile object, a lane markchanged points object, a road edge changed point object, a sign landmarkobject, a pole landmark object, a lane topology object, a drivable pathborder object, a lane merge point object, a lane split point object, ora reference point change event object.

At step 4506, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) outputs the generated navigationinformation packet to one or more navigation system processorsconfigured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet. In some embodiments, step 4506 may besimilar to step 4306 of process 4300 described above. For example, oneor more navigation system processors (e.g., navigation system processor4220) may be configured to cause navigation system 4280 to execute atleast one navigational maneuver based on the information included in thenavigation information packet (e.g., a drivable path along a roadsegment). The at least one navigational maneuver may include one or moreof: maintaining a current speed of the host vehicle; maintaining acurrent heading direction of the host vehicle; changing a headingdirection of the host vehicle; changing a speed of the host vehicle;accelerating the host vehicle; or braking the host vehicle.

At step 4507, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) detects a map origin change event.For example, the at least one electronic horizon processor may beprogrammed to detect a map origin change event when it detects that thehost vehicle has traveled more than a predetermined distance from apoint represented by the initial map origin. The predetermined distancemay be in a range of 1 km to 100 km. For example, the predetermineddistance may be 10 km, 20 km, 30 km, 40 km, or 50 km.

At step 4508, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines an updated map origin andsends to the one or more navigation system processors a notificationindicative of a change from the initial map origin to the updated maporigin, in response to a detected map origin change event. For example,the at least one electronic horizon processor may be programmed totransmit a notification (e.g., a navigation information packet)including a ReferencePointChangeEvent object to the one or morenavigation system processors, indicating that map coordinates have beenchanged.

In some embodiments, the notification sent to the one or more navigationsystem processors indicative of a change from the initial map origin tothe updated map origin may include an updated navigation informationpacket including updates to the information associated with mappedfeatures included in the determined electronic horizon. The updates tothe information may be associated with the change from the initial maporigin to the updated map origin. In some embodiments, the updates tothe information associated with mapped features included in thedetermined electronic horizon may include transformations associatedwith a change in a map origin from the initial map origin to the updatedmap origin.

In some embodiments, the at least one electronic horizon processor maygenerate an updated navigation information packet after detecting achange in a group of mapped features implicated by the determinedelectronic horizon for the host vehicle. For example, the at least oneelectronic horizon processor may generate an updated navigationinformation packet including updates to the information associated withmapped features included in the determined electronic horizon in the newmap coordinate system. The at least one electronic horizon processor mayalso transmit the updated navigation information packet to the one ormore navigation system processors.

In some embodiments, after receiving the notification indicative of achange of the map origin point, the one or more navigation systemprocessors may flash the cached electronic horizon data. For example,the one or more navigation system processors may delete from the bufferthe data relating to the one or more objects included in an electronichorizon that were received from one or more previous navigationinformation packets.

Electronic Horizon: Navigation Using Points on Splines

The disclosed systems and methods may use crowdsourced informationcollected from multiple drives to generate and/or refine maps associatedwith road segments. The maps may include trajectories (e.g.,three-dimensional splines) that are available to vehicles (e.g., host ortarget/detected vehicles) traveling on a roadway associated with a map.The maps may also include detected objects (e.g., road signs, roadedges, lane markings, bus stops, or any other recognizable featureassociated with a roadway, etc.) and may associate the detected objectsin the map with refined locations associated with one or more of thedetected objects. The refined positions may be determined based oncrowd-sourced location information determined during each of a pluralityof individual drives along a road segment. The detected objects andtheir locations from the map may be used in navigating an autonomous orpartially autonomous vehicle (e.g., by assisting in determining where avehicle is located relative to a target trajectory from the map).

To generate a crowdsourced map, drive information may be collected frommultiple drives along a road segment. This may include, for example,collecting drive information from one vehicle traveling in an area atdifferent times and/or from multiple different vehicles traveling in anarea. The collected information may then be aligned to promote accuracyin filling in holes in drive data sets (e.g., caused by occlusionsduring a particular drive, etc.), refining object locations, refiningvehicle trajectories, etc. More details regarding maps and generatingmaps are provided below.

In some cases, a map (or at least a portion of a map) may be provided toa vehicle for use in navigation, and in some cases, the map features maybe expressed in a map coordinate system relative to a predeterminedorigin. For example, the system generating the map may set a particularorigin point relative to the map, and the target trajectories, detectedobjects, road edges, etc. may be referenced relative to the particularorigin selected for the map. Consequently, in such cases, when a vehiclenavigates using the map information, the navigation system of thevehicle may need to perform various calculations relative to the mappedfeatures in order to effectively use the mapped features in navigatingthe vehicle. Such calculations may include, among other things, samplingof a target trajectory in map coordinates to determine points along thetarget trajectory where the vehicle is predicted to be after certaintime intervals (e.g., 1 second, 2 seconds, 5 seconds, etc. from apresent time). The vehicle navigation system may also calculate similarpoints with respect to trajectories associated with detected targetvehicles, calculate distances to drivable paths associated with adjacentlanes, calculate distances to stop points, traffic lights, merge orsplit points, etc. In many cases, the calculations the host vehiclenavigation system performs converting or expressing aspects of thereceived map information (which is expressed in a coordinate systemrelative to the map) into local coordinates that are relative to acoordinate system associated with the particular host vehicle.

Such calculations onboard a host vehicle can lead to certain challenges.For example, in some cases, these map transformation calculations mayinvolve sophisticated processors associated with a host vehiclenavigation system, which can add cost and complexity to the system. Thecalculations may require significant computing resources, which mayotherwise be used for other navigational or vehicle-related tasks, andthe calculations may be difficult to perform at a rate suitable forreal-time navigation (e.g., full sets of point calculations relative tomultiple road segment features multiple times per second). Additionally,requiring each host vehicle navigational system to perform the mapcalculations needed navigate relative to a map may increase thecomplexity of manufacturing a vehicle navigational system and/or maylead to non-uniform or sub-optimal usage of map information innavigating different host vehicles (especially those whose navigationalsystems or system components are developed by different sources).

Thus, there exists a need for navigational systems and/or navigationalsystem map interfaces that may reduce or eliminate the burden of a hostvehicle navigational system associated with relating certain mapfeatures expressed in map coordinates to an origin associated with theparticular host vehicle. Such systems, for example, may convert certainmap elements from map coordinates to vehicle coordinates (e.g., havingan origin located at a position of a camera associated with the hostvehicle). Such systems may also generate certain standard sets of pointsexpressed in vehicle coordinates relative to map features stored in mapcoordinates (e.g., drivable paths along available lanes on a roadsegment, lane markings, road boundaries, etc.). Through automaticgeneration of such features in a vehicle coordinate system, thedescribed systems and interfaces may alleviate the need for OEMs orother entities involved in developing vehicle navigational systems todevelop sophisticated systems to analyze mapped features in mapcoordinates and do the calculations and conversions needed navigaterelative to those features from a vehicle coordinate perspective.

Disclosed systems and methods may automatically provide map elementsrelatively close to the vehicle according to a vehicle coordinatesystem. The disclosed systems and methods may also provide various typesof standard point sets in the vehicle coordinate system that may beuseful in navigating a host vehicle. In some cases, the describedsystems may provide or generate data relative to mapped road segments inthe form of 3D sampled points over splines representative of drivablepaths, lane markings, road edges, road boundaries, etc. Such generateddata may also include lane assignment information relative to the hostvehicle, lane split/merge points, stop points at intersections, etc.This system and associated protocol may be referred to as localizationfor control in the attached documents. In some cases, the location forcontrol protocol may output two or more main data types, including, forexample, 1) road edges, lane marks, drivable path traces and a set ofsampled 3D points relative to mapped splines representative of theseroad features; and 2) lane assignment information (e.g., indicators ofan ego lane associated with the host vehicle and one or two lanes to theright and/or left of the ego lane). The output of the system may beconfigurable such that the number of sampled points to output may vary,the number of lanes for which assignment information may be provided mayvary, distances between the sampled points may be varied, etc. Suchcoordinate points, which are relative to a vehicle's coordinate system,may allow for the vehicle to navigate autonomously. For example, thevehicle's navigation system may use the coordinate points for controlfunctions, such as steering, braking, and acceleration.

In some embodiments, to abstract and correlate the control points data,additional information in the form of predicted points may be outputtedby the protocol. The utilized values may include 3 points on thevehicle's drivable path in predefined times shifts. Accuracy level ofthose values may be in 1 mm level. FIG. 46 illustrates exemplarypredicted positions P1, P2, and P3 of a vehicle at time points t0, t1,and t2, respectively. In some embodiments, t0, t1, and t2 are predefinedtime shifts.

In some embodiments, the points in a map may be 3D sampled points of theDP, LM, RE splines in the vehicle coordinate system. The points outputmay be based on a predefined set of properties, which may beconfigurable in the development phase. A navigation information packetmay include a control point object. By way of example, Table 31 belowillustrates an exemplary control point object.

TABLE 31 Control points object description Control Point [ ] U value[uint16] X [uint16] Y [uint16] Z [uint16] Spline ID [uint32]

In some embodiments, each set of points may be outputted with additionaldata, such as the spline type [DP=0, LM=1, RE=2], the number of printedpoints on the spline (within a specific frame), a spline index—lanenumbering relative to ego lane. (ego is 0, then left lanes are −1, −2 .. . . And right lanes are 1, 2, 3 etc.), an oncoming Boolean flagindicating a driving direction on the lane, etc.

To produce local coordinates, the vehicle's navigation system mayprocess information collected from the environment of the vehicle. Oneor more sensors, such as cameras, radar, and LIDAR may collectinformation as the vehicle travels through the environment. Thecollection information may include the position of various detectedobjects in the environment of the vehicle, such as road signs, roadedges, lane markings, bus stops, or any other recognizable featureassociated with a roadway, etc. The vehicle's navigation system may thenanalyze the collected information and use the information in comparisonto mapped information (e.g., representations of mapped features in a mapexpressed in map coordinates) and may determine coordinates for thedetected objects that are relative to a coordinate system of the vehicleand/or may determine sets of 3D sampled points relative to mappedfeatures such as drivable paths, etc., as described above. For example,such coordinate points may be output in a vehicle coordinate system inwhich a camera on board the vehicle is designated the origin of thecoordinate system. The format of the points may thus be expressed as x,y, and z values relative to the host vehicle camera position used as theorigin for expression of road features or sampled points in the vehiclecoordinate system. By using a coordinate system that is local to thevehicle while it navigates, and by generating standard sets ofinformation (e.g., 3D sampled points along drivable paths, etc., usefulin navigating the vehicle), the vehicle's navigation system outside ofthe location for control hardware/interface may be required to performfewer navigational calculations relative to mapped features duringnavigation of the vehicle, thus efficiently managing computingresources.

As discussed earlier, a map may include trajectories such as drivablepaths, road edges, lane markings, etc., which may be represented bythree-dimensional splines in the map. The map may also include locationinformation for certain objects (e.g., road signs, road edges, lanemarkings, bus stops, or any other recognizable features associated witha roadway, etc.) located on or associated with a road system. Thedisclosed systems and methods may sample points on the splines andproject those points onto a map in the vehicle's coordinate system. Todo so, the disclosed systems and methods may use a rotation translationmatrix to transform points from a map coordinate system (e.g.,coordinates relative to a map) into a vehicle coordinate system.

For example, x, y, and x may represent the location of the vehicle inthe map. A translation of those points may result in coordinatesrelative to the vehicle's coordinate system. As one example, when asensor onboard the vehicle detects lane marks, road edges, and/or adrivable path, the disclosed systems and methods may insert points onthese lines.

Furthermore, the disclosed systems and methods make use of alongitudinal component. This technique may be used to sample a spline toarrive at the x, y, z points discussed above. For example, an algorithmmay be used to evaluate a longitudinal parameter and to calculate x, y,and z points relative to the spline at a particular longitudinal value.

In order for the vehicle to navigate based on, for example, athree-dimensional spline relative to a map, the vehicle's navigationsystem may need to make various calculations. The disclosed systems andmethods include a map interface and protocol system that relieves othernavigational computing systems (e.g., those provided by OEMs, etc.) fromhaving to sample the splines to generate sampled points in the vehiclecoordinate system used during navigation, which is an expensivecomputational process, especially under the time constraints ofreal-time driving and navigation.

In some embodiments, points may be structured in arrays with indicesidentifying what they represent. For example, in some cases, a sampledset of points may represent a road edge, lane marking, drivable path,etc. In some embodiments, each array of points may be associated with adifferent road feature. Additionally or alternatively, the system mayoutput an identifier of the original spline that the points belong to(which may be useful in enabling a client navigational system tocorrelate between supplied arrays of points and mapped features in mapcoordinates supplied according to the electronic horizon protocol). Insome cases, a camera-based navigational system may detect lane marks,road edges, etc. and may be provided with a target trajectoryrepresentative of the mapped drivable path associated with the ego laneof the host vehicle. The disclosed system may use the mapped information(e.g., maps) to calculate sets of points in the vehicle coordinatesystem relative to these mapped features that are also detected by thecamera-based navigational system. These points may then be used by thenavigational system in controlling the path of the host vehicle tofollow the target trajectory, change lanes, stay within road boundaries,etc.

As discussed, the disclosed systems may output sampled points forvarious road features. These arrays of points may be associated with thehost vehicle ego lane. The arrays of points, however, may also beassociated with lanes to the left or right of the host vehicle lane andmay also be associated with lanes traveling in the opposite direction toor intersecting with the host vehicle lane. In such cases, the arrays ofsampled points for drivable paths, lane markings, etc. associated withother lanes may be indicated through one or more indices (e.g., +1 forone lane to the right, +2 for two lanes to the right, −1 for one lane tothe left, −2 for two lanes to the left, etc.). Each array of points maybe associated with a different road feature. Such information can beuseful in navigating the host vehicle through a lane change maneuver, asthe host vehicle may have available sampled points for a drivable pathin a target lane prior to making the lane change maneuver. Suchinformation may also assist the host vehicle in navigating relative todetected target vehicles, as the host vehicle systems may be able todetermine or predict a path one or more target vehicles will travelbased on mapped and sampled drivable paths for adjacent or intersectinglanes. The mapped and sampled information may also be useful when thehost vehicle is collecting information relating to a road segmenting(e.g., automatically determining and reporting on an average speedtraveled within a certain lane by the host vehicle, target vehicles,etc.).

Other types of points associated with road features may be important forvehicle navigation. Such points, for example, may include lane split ormerge points and/or vehicle stop points at traffic lights orintersections (e.g., at any point at which a drivable path crosses astop line), among others. In some cases, these points may fall betweenor within the spacing associated with sampled points along a roadfeature spline. Therefore, the disclosed systems may be configured tooutput these types of points, among others, in addition to the sampledpoints along one or more splines stored in a map. Such points may beused in navigating relative to lane splits/merges or stop points evenbeyond where such points and/or the associated road features may bedetected by an onboard camera. At merge/split points and/or stop points,the disclosed systems and methods may provide point locations with ahigh degree of accuracy (e.g., 1 cm accuracy) in view of thecrowd-sourced nature by which these types of points were determined andmapped.

The output points of the presently described system and system interfacemay be user configurable. For example, a distance between points may beselected or controlled based on vehicle speed. At slower speeds, thedisclosed systems and methods may use a closer sample spacing. On theother hand, at faster speeds, the disclosed systems and methods mayincrease space to “see” further down road for control. For example, thesystem may provide more points at faster speeds. The sampled points maybe client selectable or user selectable. For example, the sampled pointsmay correspond to locations along a drivable path where the host vehicleis projected to be at 1 second, 2 seconds, 3 seconds, etc. Otherspacings, such as 0.5 seconds, 1.5 seconds, 2.5 seconds, or any otherregular or irregular spacings may also be used.

FIG. 47 is a flowchart showing an exemplary process for navigating avehicle, consistent with disclosed embodiments. One or more steps ofprocess 4700 may be performed by a vehicle (e.g., vehicle 2702), adevice (e.g., vehicle device 2703) associated with the host vehicle, atleast one processor (e.g., an electronic horizon processor), and/or aserver (e.g., server 2701). While the descriptions of process 4700provided below use vehicle 2702 (or one or more components thereof) asan example, one skilled in the art would appreciate that one or moresteps of process 4700 may be performed by a server and/or a vehicledevice. For example, in an embodiment, a system for navigating a vehicleincludes at least one processor (e.g., an electronic horizon processor)comprising circuitry and a memory. The memory includes instructions thatwhen executed by the circuitry cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 47. As another example, in anotherembodiment, a non-transitory computer readable medium containsinstructions that, when executed by at least one processor (e.g., anelectronic horizon processor), may cause the at least one processor toperform one or more operations, such as any of the operations discussedherein and/or in connection with FIG. 47.

At step 4701, at least one electronic horizon processor (e.g.,electronic horizon processor 4210) accesses a map representative of atleast a road segment on which the host vehicle travels or is expected totravel. The map may include one or more splines representative of roadfeatures associated with the road segment. In some embodiments, a splinemay represent at least one of one or more of: a drivable path for thehost vehicle, a road edge, or a lane mark. For example, the one or moresplines may include a representation of the drivable path for the hostvehicle, and the drivable path for the host vehicle may be associatedwith an ego lane of the road segment in which the host vehicle islocated. Alternatively or additionally, the one or more splines mayinclude a representation of one or more potential drivable paths for thehost vehicle, and the one or more potential drivable paths may beassociated with lanes of the road segment different from an ego lane ofthe road segment in which the host vehicle is located. Alternatively oradditionally, the one or more splines may include a representation of aroad edge associated with the road segment. Alternatively oradditionally, the one or more splines may include a representation of alane mark associated with the road segment.

In some embodiments, step 4701 may be similar to step 4301 of process4300 described above. For example, the at least one electronic horizonprocessor may be programmed to receive a map from storage device 4240and/or memory 4230. Alternatively or additionally, the at least oneelectronic horizon processor may receive the map data from a server(e.g., server 2701) and/or a database (e.g., database 2704), which maybe remotely located relative to vehicle 2702, via network 2705. In someembodiments, the map accessed by the at least one electronic horizonprocessor may represent a sub-segment of a larger map available on a mapserver (e.g., server 2701) remotely located relative to the hostvehicle.

In some embodiments, the accessed map may include a plurality ofthree-dimensional splines associated with one or more road segments, andthe three-dimensional splines may represent one or more of: a drivablepath for the host vehicle, a road edge, or a lane mark.

In some embodiments, the accessed map is generated based oncrowd-sourced drive information collected from a plurality of vehiclesthat traversed the road segment on which the host vehicle travels or isexpected to travel prior to the host vehicle (as described elsewhere inthis disclosure).

In some embodiments, the accessed map may include a plurality ofthree-dimensional point locations specified in a map coordinate systemassociated with the accessed map (e.g., a geographic coordinate systemsuch as GPS coordinate system). One or more of the objects included inthe payload portion of the navigation packet may include athree-dimensional point location specified in a coordinate systemassociated with the host vehicle (e.g., a coordinate system local to thehost vehicle). For example, the coordinate system associated with thehost vehicle may have the position of one of the one or more cameras ofthe host vehicle (or the center of the host vehicle) as the origin ofthe coordinate system.

At step 4702, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) receives an output provided by atleast one vehicle sensor. In some embodiments, step 4702 may be similarto step 4302 of process 4300. For example, the at least one vehiclesensor may include one or more cameras configured to capture images ofan environment of the host vehicle. By way of example, vehicle 2702 mayinclude one or more cameras similar to image capture device 122, imagecapture device 124, and image capture device 126 described above, whichmay be configured to capture images of an environment of the hostvehicle. The received output may include at least one image captured bythe one or more cameras.

In some embodiments, the at least one vehicle sensor may also includeother types of sensors, including, for example, a speed sensor, anacceleration sensor, a GPS receiver, a radar sensor, a LIDAR sensor, orthe like, or a combination thereof.

At step 4703, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) localizes the host vehicle relativeto the map or a feature thereof (e.g., a drivable path) for the hostvehicle represented among the one or more splines. The localization maybe based on analysis of the at least one image captured by the one ormore cameras. In some embodiments, step 4703 may be similar to step 4303of process 4300 and/or step 4403 of process 4400 described above. Forexample, the at least one electronic horizon processor may localize thehost vehicle relative to a drivable path based on analysis of the atleast one image captured by the one or more cameras. By way of example,the at least one electronic horizon processor may determine a positionof the host vehicle relative to a mapped drivable path based on anidentification of one or more mapped landmarks represented in the atleast one captured image and a comparison of an image position of theone or more mapped landmarks in the at least one captured image with anexpected image position of the one or more mapped landmarks associatedwith at least one position along the drivable path. Alternatively oradditionally, the localization of the host vehicle relative to the atleast one mapped feature may include determining a position of the hostvehicle relative to a mapped drivable path based on an identification ofone or more mapped landmarks represented in two or more captured imagesand based on a motion history of the host vehicle during a time periodbetween when the two or more captures images were acquired.

At step 4704, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) determines a set of points associatedwith the one or more splines based on the localization of the hostvehicle relative to the drivable path for the host vehicle. For example,the at least one electronic horizon processor may be programmed todetermine a set of points including one or more predicted locations ofthe host vehicle relative to the drivable path for the host vehicle(e.g., the predicted locations P1, P2, and P3 of the host vehicleillustrated in FIG. 46). Alternatively or additionally, the one or morepredicted locations of the host vehicle may be associated withpredetermined future time intervals (e.g., every 0.5 seconds, 1 second,2 seconds, 5 seconds, 10 seconds, 30 seconds, 1 minute, 5 minutes,etc.). In some embodiments, the one or more predicted locations of thehost vehicle may be determined based on a current speed of the hostvehicle. Alternatively or additionally, the one or more predictedlocations of the host vehicle may be determined based on a planned speedprofile of the host vehicle (e.g., the average speed and/or the speedlimit on that point (the longitudinal position) on a drivable path).

In some embodiments, the determined set of points may include a pointlocation associated with an intersection of the drivable path for thehost vehicle with a stop line represented in the accessed map.Alternatively or additionally, the determined set of points may includea point location associated with a lane split feature of the roadsegment.

Alternatively or additionally, the determined set of points may includea point location associated with a lane merge feature of the roadsegment.

Alternatively or additionally, the determined set of points may includea point location associated with an intersection of a potential drivablepath for the host vehicle, in a lane different from an ego lane in whichthe host vehicle is located, with a stop line represented in theaccessed map. Alternatively or additionally, the determined set ofpoints may include three-dimensional points referenced a coordinatesystem relative to the host vehicle. For example, the determined set ofpoints may include three-dimensional points referenced a coordinatesystem having an origin associated with one of the one or more cameras.

At step 4705, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) generates a navigation informationpacket. The generated navigation information packet may includeinformation associated with the one or more splines and the determinedset of points relative to the one or more splines. In some embodiments,a navigation information packet may include other types of informationdescribed in this disclosure. For example, a navigation informationpacket may include a header portion and a payload portion. The headerportion may specify what information is included in the payload portion.The payload portion may include information relating to one or moreobjects associated with the road segment(s). In some embodiments, thepayload portion may be variable-sized. In some embodiments, the headerportion of the navigation information packet may include informationidentifying objects included in the payload portion, a size of each ofthe objects included in the payload portion, an indication of a numberof objects included in the payload portion, or the like, or acombination thereof. By way of example, Table 5 and Table 6, discussedabove, illustrate various types of information that may be included in aheader portion, the detailed descriptions of which are repeated here forbrevity purposes. The payload portion may include one or more objectsassociated with the road segment(s) included in the determinedelectronic horizon. The objects included in the payload portion mayinclude one or more of: a spline object, a drivable path profile object,a lane mark changed points object, a road edge changed point object, asign landmark object, a pole landmark object, a lane topology object, adrivable path border object, a lane merge point object, a lane splitpoint object, or a reference point change event object. In someembodiments, one or more objects included in the payload portion may begenerated by the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) based on the determined electronichorizon. For example, one or more objects included in the payloadportion may be generated by the at least one electronic horizonprocessor (e.g., electronic horizon processor 4210) based on featuresassociated with the accessed map that are included within the determinedelectronic horizon.

In some embodiments, the navigation information packet may correlate theone or more splines with lanes of travel associated with the roadsegment. For example, the navigation information packet may identify anego lane of travel in which the host vehicle is located. In someembodiments, the navigation information packet may index one or moreadditional lanes of travel relative to the ego lane (e.g., the indexesand/or sub-indexes illustrated in FIG. 38 discussed above). In someembodiments, the navigation information packet may include a Booleanvalue associated with at least one lane of travel, and the Boolean valuemay indicate whether a travel direction associated with the at least onelane of travel is in a same direction as a travel direction associatedwith the ego lane.

At step 4706, the at least one electronic horizon processor (e.g.,electronic horizon processor 4210) outputs the generated navigationinformation packet to one or more navigation system processorsconfigured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet. In some embodiments, step 4706 may besimilar to step 4306 of process 4300 described above. For example, oneor more navigation system processors may be configured to causenavigation system 4280 to execute at least one navigational maneuverbased on the information included in the navigation information packet(e.g., a drivable path along a road segment). The at least onenavigational maneuver may include one or more of: maintaining a currentspeed of the host vehicle; maintaining a current heading direction ofthe host vehicle; changing a heading direction of the host vehicle;changing a speed of the host vehicle; accelerating the host vehicle; orbraking the host vehicle.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-90. (canceled)
 91. A system for navigating a host vehicle, the systemcomprising: at least one electronic horizon processor comprisingcircuitry and a memory, wherein the memory includes instructions thatwhen executed by the circuitry cause the at least one electronic horizonprocessor to: access a map representative of at least a road segment onwhich the host vehicle travels or is expected to travel, wherein the mapincludes one or more splines representative of road features associatedwith the road segment; receive an output provided by at least onevehicle sensor, wherein the at least one vehicle sensor includes one ormore cameras configured to capture images of an environment of the hostvehicle, and wherein the received output includes at least one imagecaptured by the one or more cameras; localize the host vehicle relativeto a drivable path for the host vehicle represented among the one ormore splines, wherein the localization is based on analysis of the atleast one image captured by the one or more cameras; determine a set ofpoints associated with the one or more splines based on the localizationof the host vehicle relative to the drivable path for the host vehicle;generate a navigation information packet including informationassociated with the one or more splines and the determined set of pointsrelative to the one or more splines; and output the generated navigationinformation packet to one or more navigation system processorsconfigured to cause the host vehicle to execute at least onenavigational maneuver based on the information included in thenavigation information packet.
 92. The system of claim 91, wherein theone or more splines include a representation of the drivable path forthe host vehicle, and wherein the drivable path for the host vehicle isassociated with an ego lane of the road segment in which the hostvehicle is located.
 93. The system of claim 91, wherein the one or moresplines include a representation of one or more potential drivable pathsfor the host vehicle, and wherein the one or more potential drivablepaths are associated with lanes of the road segment different from anego lane of the road segment in which the host vehicle is located. 94.The system of claim 91, wherein the one or more splines include arepresentation of a road edge associated with the road segment.
 95. Thesystem of claim 91, wherein the one or more splines include arepresentation of a lane mark associated with the road segment.
 96. Thesystem of claim 91, wherein the set of points includes one or morepredicted locations of the host vehicle relative to the drivable pathfor the host vehicle.
 97. The system of claim 96, wherein the one ormore predicted locations of the host vehicle are determined based on acurrent speed of the host vehicle.
 98. The system of claim 96, whereinthe one or more predicted locations of the host vehicle are determinedbased on a planned speed profile of the host vehicle.
 99. The system ofclaim 96, wherein the one or more predicted locations of the hostvehicle are associated with predetermined future time intervals. 100.The system of claim 91, wherein the set of points includes one or morepredicted locations of the host vehicle relative to a drivable path forat least one lane of the road segment different from an ego lane inwhich the host vehicle is located.
 101. The system of claim 91, whereinthe set of points includes a point location associated with anintersection of the drivable path for the host vehicle with a stop linerepresented in the accessed map.
 102. The system of claim 91, whereinthe set of points includes a point location associated with a lane splitfeature of the road segment.
 103. The system of claim 91, wherein theset of points includes a point location associated with a lane mergefeature of the road segment.
 104. The system of claim 91, wherein theset of points includes a point location associated with an intersectionof a potential drivable path for the host vehicle, in a lane differentfrom an ego lane in which the host vehicle is located, with a stop linerepresented in the accessed map.
 105. The system of claim 91, whereinthe accessed map is generated based on crowd-sourced drive informationcollected from a plurality of vehicles that traversed the road segmentprior to the host vehicle.
 106. The system of claim 91, wherein theaccessed map is received by the electronic horizon processor from a mapserver remotely located relative to the host vehicle.
 107. The system ofclaim 91, wherein the accessed map represents only a sub-segment of alarger map available on a map server remotely located relative to thehost vehicle.
 108. The system of claim 91, wherein localization of thehost vehicle relative to the drivable path for the host vehicle includesdetermining a position of the host vehicle relative to the drivable pathfor the host vehicle based on an identification of one or more mappedlandmarks represented in the at least one captured image and acomparison of an image position of the one or more mapped landmarks inthe at least one captured image with an expected image position of theone or more mapped landmarks associated with at least one position alongthe drivable path for the host vehicle.
 109. The system of claim 91,wherein the localization of the host vehicle relative to the drivablepath for the host vehicle includes determining a position of the hostvehicle relative to the drivable path for the host vehicle based on anidentification of one or more mapped landmarks represented in two ormore captured images and based on a motion history of the host vehicleduring a time period between when the two or more captures images wereacquired.
 110. The system of claim 91, wherein the at least onenavigational maneuver includes one or more of: maintaining a currentspeed of the host vehicle; maintaining a current heading direction ofthe host vehicle; changing a heading direction of the host vehicle;changing a speed of the host vehicle; accelerating the host vehicle; orbraking the host vehicle.
 111. The system of claim 91, wherein the atleast one vehicle sensor includes a speed sensor.
 112. The system ofclaim 91, wherein the at least one vehicle sensor includes a GPSreceiver.
 113. The system of claim 91, wherein the set of points includethree-dimensional points referenced a coordinate system relative to thehost vehicle.
 114. The system of claim 113, wherein an origin of thecoordinate system is associated with one of the one or more cameras.115. The system of claim 91, wherein the navigation information packetcorrelates the one or more splines with lanes of travel associated withthe road segment.
 116. The system of claim 115, wherein the navigationinformation packet identifies an ego lane of travel in which the hostvehicle is located.
 117. The system of claim 116, wherein the navigationinformation packet indexes one or more additional lanes of travelrelative to the ego lane.
 118. The system of claim 116, wherein thenavigation information packet includes a Boolean value associated withat least one lane of travel, and wherein the Boolean value indicateswhether a travel direction associated with the at least one lane oftravel is in a same direction as a travel direction associated with theego lane.
 119. A non-transitory computer readable medium containinginstructions that when executed by at least one electronic horizonprocessor, cause the at least one electronic horizon processor toperform operations comprising: accessing a map representative of atleast a road segment on which a host vehicle travels or is expected totravel, wherein the map includes one or more splines representative ofroad features associated with the road segment; receiving an outputprovided by at least one vehicle sensor, wherein the at least onevehicle sensor includes one or more cameras configured to capture imagesof an environment of the host vehicle, and wherein the received outputincludes at least one image captured by the one or more cameras;localizing the host vehicle relative to a drivable path for the hostvehicle represented among the one or more splines, wherein thelocalization is based on analysis of the at least one image captured bythe one or more cameras; determining a set of points associated with theone or more splines based on the localization of the host vehiclerelative to the drivable path for the host vehicle; generating anavigation information packet including information associated with theone or more splines and the determined set of points relative to the oneor more splines; and outputting the generated navigation informationpacket to one or more navigation system processors configured to causethe host vehicle to execute at least one navigational maneuver based onthe information included in the navigation information packet.